In [75]:
library(data.table)
library(clusterProfiler)
library(dplyr)
library(Seurat)
library(ggplot2)
library(circlize)
In [76]:
library(org.Hs.eg.db)
library(msigdbr)
Loading required package: AnnotationDbi

Loading required package: stats4

Loading required package: BiocGenerics


Attaching package: ‘BiocGenerics’


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    anyDuplicated, aperm, append, as.data.frame, basename, cbind,
    colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
    get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
    table, tapply, union, unique, unsplit, which.max, which.min


Loading required package: Biobase

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Loading required package: IRanges

Loading required package: S4Vectors


Attaching package: ‘S4Vectors’


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Attaching package: ‘AnnotationDbi’


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In [77]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>% 
  dplyr::select(gs_name, entrez_gene)
head(m_t2g)
A tibble: 6 × 2
gs_nameentrez_gene
<chr><int>
HALLMARK_ADIPOGENESIS 19
HALLMARK_ADIPOGENESIS11194
HALLMARK_ADIPOGENESIS10449
HALLMARK_ADIPOGENESIS 33
HALLMARK_ADIPOGENESIS 34
HALLMARK_ADIPOGENESIS 35
In [3]:
fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/all_driver_types_match_counts.csv')
A data.table: 10 × 7
seedshap_totalshap_0shap_1grad_totalgrad_0grad_1
<int><int><int><int><int><int><int>
0252525222423
1242625232424
2242325202418
3242525252425
4242426222023
5242426202418
6242424222423
7242426222623
8222323232524
9232325232324
In [4]:
fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/driver_summary_shap_total.csv')
A data.table: 63 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
FN1 0.0079933780.0007537607 1.907695e-0410FALSEFALSEFALSEFALSE
HIST1H2BD0.0038648270.0005008594 1.813786e-0410FALSEFALSEFALSEFALSE
CCND1 0.0023067440.0002728280 1.020445e-0410FALSEFALSEFALSEFALSE
KRT8 0.0041713730.0006077977 7.349687e-0510FALSEFALSEFALSEFALSE
GSTM3 0.0065702270.0004776801 1.470234e-0410FALSEFALSEFALSEFALSE
CD9 0.0042869820.0004431204 1.179431e-0410FALSEFALSEFALSEFALSE
H2AFZ 0.0014095510.0002377385 4.583457e-0510FALSE TRUEFALSE TRUE
LAPTM4A 0.0055573910.0006989103 1.191966e-0410FALSEFALSEFALSEFALSE
MDK 0.0044854940.0007073663 9.334373e-0510FALSEFALSEFALSEFALSE
PRDX1 0.0057532930.0005948305 1.307083e-0410FALSEFALSE TRUE TRUE
PGK1 0.0029370070.0004105406 8.659618e-0510FALSEFALSEFALSEFALSE
NQO1 0.0071891720.0005834166 1.745988e-0410FALSEFALSE TRUE TRUE
ALPP 0.0037903640.0003220504 1.560096e-0410FALSEFALSEFALSEFALSE
MT2A 0.0029928150.0007070334-5.982608e-0510FALSEFALSEFALSEFALSE
PKM 0.0024184760.0004418046 8.013423e-0510FALSEFALSEFALSEFALSE
UBB 0.0020930290.0004443382-5.899295e-0510FALSEFALSEFALSEFALSE
FOSL1 0.0042657690.0005058976-9.429040e-0510 TRUEFALSEFALSEFALSE
CTSA 0.0040279710.0005851937 1.295741e-0410FALSEFALSEFALSEFALSE
UBE2S 0.0047840570.0005292148-1.516910e-0410FALSEFALSEFALSEFALSE
HSPB1 0.0020172960.0003566292 7.373099e-0510FALSEFALSEFALSEFALSE
MYL6 0.0085084580.0008462955 1.682113e-0410FALSEFALSEFALSEFALSE
TCOF1 0.0022865290.0003146553-6.928714e-0510FALSEFALSEFALSEFALSE
TFDP1 0.0033304280.0003013794-9.927206e-0510 TRUEFALSEFALSEFALSE
CSTB 0.0031482890.0003750370-1.198390e-0410FALSEFALSEFALSEFALSE
JUND 0.0016843240.0003056773 1.057126e-0410 TRUEFALSEFALSEFALSE
TGM2 0.0053295820.0004317851-1.178054e-0410FALSEFALSEFALSEFALSE
SQSTM1 0.0028696040.0004758771 9.440278e-0510FALSEFALSEFALSEFALSE
HIST1H4C 0.0037515880.0004830967-1.076414e-0410FALSEFALSEFALSEFALSE
CKS1B 0.0026070020.0003412548 9.403953e-05 9FALSEFALSEFALSEFALSE
CD24 0.0027278510.0002715580 1.166907e-05 9FALSEFALSEFALSEFALSE
⋮⋮⋮⋮⋮⋮⋮⋮⋮
CENPN 0.00212675123.911722e-04 8.123492e-058FALSEFALSEFALSEFALSE
HMGA1 0.00125680325.449500e-04-4.842230e-057 TRUEFALSEFALSEFALSE
CD81 0.00268137972.755875e-04 1.560641e-047FALSEFALSEFALSEFALSE
PPIF 0.00139789991.616978e-04-1.086447e-056FALSEFALSEFALSEFALSE
PSMB6 0.00158162401.423927e-04 9.055104e-055FALSEFALSEFALSEFALSE
CDK1 0.00078929861.556827e-04-6.600696e-055FALSEFALSEFALSEFALSE
CCNB1 0.00096002527.355914e-05 3.018131e-075FALSEFALSEFALSEFALSE
SERPINH10.00113318892.452786e-04 5.132065e-055FALSEFALSEFALSEFALSE
FOXM1 0.00066866761.551108e-04-7.297643e-065 TRUEFALSEFALSEFALSE
LAMP1 0.00146759473.839450e-04 7.594953e-055FALSEFALSEFALSEFALSE
PPP1R14B0.00119375281.994650e-04-8.283027e-064FALSEFALSEFALSEFALSE
TFPI2 0.00116053802.944522e-04 7.088958e-054FALSEFALSEFALSEFALSE
ALDH3A1 0.00130553771.966169e-04 4.354592e-074FALSE TRUEFALSE TRUE
CALD1 0.00067213599.202680e-06 3.342999e-073FALSEFALSEFALSEFALSE
TUBA4A 0.00058295211.696022e-04 1.596368e-073FALSEFALSEFALSEFALSE
IGFBP3 0.00102586542.486552e-04 1.015227e-053FALSEFALSEFALSEFALSE
TUBB4B 0.00038309044.519722e-05 7.412989e-082FALSEFALSEFALSEFALSE
MYBL2 0.00024020411.968738e-04 6.863686e-062 TRUEFALSEFALSEFALSE
PHGDH 0.00041736705.805091e-05 2.442747e-072FALSEFALSEFALSEFALSE
UBC 0.00021020676.868151e-05 1.274175e-072FALSEFALSEFALSEFALSE
PCNA 0.00044944111.240919e-04 5.007451e-052FALSEFALSEFALSEFALSE
HIST1H1C0.00048376571.441257e-04 9.379254e-062FALSEFALSEFALSEFALSE
CTNNB1 0.00013813280.000000e+00-9.900118e-091FALSEFALSEFALSEFALSE
CALR 0.00024549710.000000e+00-1.203399e-071FALSEFALSEFALSEFALSE
MCM3 0.00017890020.000000e+00 2.958242e-101FALSEFALSEFALSEFALSE
CENPF 0.00023769770.000000e+00 6.111640e-091FALSEFALSEFALSEFALSE
ANXA2 0.00018179790.000000e+00-2.258579e-101FALSEFALSEFALSEFALSE
TOP2A 0.00026935510.000000e+00-8.096725e-101FALSEFALSEFALSEFALSE
NDC80 0.00022566190.000000e+00-6.941362e-081FALSEFALSEFALSEFALSE
SMC4 0.00019395610.000000e+00-6.988958e-081FALSEFALSEFALSEFALSE
In [5]:
fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/driver_summary_grad_total.csv')
A data.table: 72 × 9
V1weight_grad_total_meanweight_grad_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
FN1 0.00331950130.0006092176 1.907695e-0410FALSEFALSEFALSEFALSE
TGM2 0.00247236180.0002408337-1.178054e-0410FALSEFALSEFALSEFALSE
FOSL1 0.00213866240.0003259176-9.429040e-0510 TRUEFALSEFALSEFALSE
TUBA1A 0.00142713710.0003019460-6.416579e-0510FALSEFALSEFALSEFALSE
NQO1 0.00310870010.0004006423 1.745988e-0410FALSEFALSE TRUE TRUE
PRDX1 0.00279947870.0002450953 1.307083e-0410FALSEFALSE TRUE TRUE
HIST1H2BD0.00259279660.0007508685 1.813786e-0410FALSEFALSEFALSEFALSE
MDK 0.00222885540.0003639519 9.334373e-0510FALSEFALSEFALSEFALSE
LAPTM4A 0.00213885180.0002744455 1.191966e-0410FALSEFALSEFALSEFALSE
H2AFZ 0.00103188360.0003560341 4.583457e-0510FALSE TRUEFALSE TRUE
GSTM3 0.00257236060.0002970675 1.470234e-0410FALSEFALSEFALSEFALSE
KRT8 0.00178386590.0003099043 7.349687e-0510FALSEFALSEFALSEFALSE
CCND1 0.00187849310.0003017914 1.020445e-0410FALSEFALSEFALSEFALSE
UBE2S 0.00242240870.0004112686-1.516910e-0410FALSEFALSEFALSEFALSE
HIST1H4C 0.00195073860.0004960266-1.076414e-0410FALSEFALSEFALSEFALSE
SQSTM1 0.00166228260.0003259419 9.440278e-0510FALSEFALSEFALSEFALSE
MYL6 0.00340691350.0003737256 1.682113e-0410FALSEFALSEFALSEFALSE
JUND 0.00192970220.0004503811 1.057126e-0410 TRUEFALSEFALSEFALSE
TFDP1 0.00204516690.0002417682-9.927206e-0510 TRUEFALSEFALSEFALSE
CSTB 0.00192388650.0002683021-1.198390e-0410FALSEFALSEFALSEFALSE
TCOF1 0.00146768900.0003327820-6.928714e-0510FALSEFALSEFALSEFALSE
CD9 0.00181831520.0002345082 1.179351e-04 9FALSEFALSEFALSEFALSE
PKM 0.00137749770.0002139187 8.007810e-05 9FALSEFALSEFALSEFALSE
PPIF 0.00159204780.0003511012-1.100813e-05 9FALSEFALSEFALSEFALSE
LDHB 0.00130977030.0002789830 6.335115e-05 8FALSEFALSEFALSEFALSE
CD81 0.00232390440.0002641056 1.738500e-04 8FALSEFALSEFALSEFALSE
ALPP 0.00204654550.0003523491 1.559023e-04 8FALSEFALSEFALSEFALSE
CDK1 0.00109836610.0002357872-6.599018e-05 8FALSEFALSEFALSEFALSE
FOXM1 0.00101648190.0003322221-4.373933e-05 8 TRUEFALSEFALSEFALSE
ATF3 0.00072967260.0001532419 4.546158e-05 7 TRUEFALSEFALSEFALSE
⋮⋮⋮⋮⋮⋮⋮⋮⋮
CTNNB1 6.839809e-042.792041e-04-3.814569e-056FALSEFALSEFALSEFALSE
CTSA 1.311533e-032.833791e-04 1.293621e-046FALSEFALSEFALSEFALSE
TUBA1B 6.988455e-041.660443e-04-2.186076e-075FALSEFALSEFALSEFALSE
KLF4 6.236043e-043.061658e-04-2.170733e-075 TRUEFALSEFALSEFALSE
CALR 6.278391e-044.215074e-04-4.846227e-074FALSEFALSEFALSEFALSE
UBB 4.775177e-041.961124e-04-5.885883e-054FALSEFALSEFALSEFALSE
GATA2 3.396291e-041.058573e-04 4.667523e-054 TRUEFALSEFALSEFALSE
NDC80 7.728954e-041.148955e-04-2.418145e-074FALSEFALSEFALSEFALSE
NME1 7.228874e-042.816583e-04 4.719073e-074FALSEFALSEFALSEFALSE
GABARAPL13.815320e-047.253918e-05 6.670668e-053FALSE TRUEFALSE TRUE
TUBA4A 3.588999e-041.403488e-04 1.846510e-073FALSEFALSEFALSEFALSE
HMGA2 5.102370e-041.755273e-04-1.525581e-073 TRUEFALSEFALSEFALSE
SMC4 2.576033e-045.121550e-05-1.792321e-072FALSEFALSEFALSEFALSE
AURKB 2.642782e-046.619854e-05-1.687990e-082FALSEFALSEFALSEFALSE
HSPA1A 2.431559e-042.353959e-05-1.418870e-082FALSEFALSEFALSEFALSE
CENPN 3.170966e-043.055441e-05 7.491441e-052FALSEFALSEFALSEFALSE
AURKA 2.958436e-042.257436e-04-5.137581e-082FALSEFALSEFALSEFALSE
EGR1 2.701469e-041.846625e-04-1.105947e-082 TRUEFALSEFALSEFALSE
HMGA1 1.855564e-044.237684e-05-4.394165e-052 TRUEFALSEFALSEFALSE
APP 1.230560e-040.000000e+00 7.437055e-061FALSEFALSEFALSEFALSE
MYBL2 8.287637e-050.000000e+00-5.011473e-091 TRUEFALSEFALSEFALSE
CCNA2 1.374829e-040.000000e+00-7.023238e-091FALSEFALSEFALSEFALSE
CEBPB 7.743918e-050.000000e+00-1.706378e-081 TRUEFALSEFALSEFALSE
GTSE1 1.651362e-040.000000e+00-9.854612e-091FALSEFALSEFALSEFALSE
LAMP1 1.789470e-040.000000e+00 9.990827e-081FALSEFALSEFALSEFALSE
SERPINH1 1.432284e-040.000000e+00 5.493789e-081FALSEFALSEFALSEFALSE
JUNB 1.218797e-040.000000e+00 1.201996e-071 TRUEFALSE TRUE TRUE
CCNB1 1.349889e-040.000000e+00 2.435022e-071FALSEFALSEFALSEFALSE
CALD1 1.464856e-040.000000e+00 8.800959e-091FALSEFALSEFALSEFALSE
GSN 1.579424e-040.000000e+00-1.304140e-071FALSEFALSEFALSEFALSE
In [151]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
read_dir <- file.path(read_dir,run_name)
In [152]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
FN1 0.0079933780.00075376071.907695e-0410FALSEFALSEFALSEFALSE
HIST1H2BD0.0038648270.00050085941.813786e-0410FALSEFALSEFALSEFALSE
CCND1 0.0023067440.00027282801.020445e-0410FALSEFALSEFALSEFALSE
KRT8 0.0041713730.00060779777.349687e-0510FALSEFALSEFALSEFALSE
GSTM3 0.0065702270.00047768011.470234e-0410FALSEFALSEFALSEFALSE
CD9 0.0042869820.00044312041.179431e-0410FALSEFALSEFALSEFALSE
In [9]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("UNIPROT","ENTREZID"), OrgDb="org.Hs.eg.db")
'select()' returned 1:many mapping between keys and columns

Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("UNIPROT", "ENTREZID"), :
“6.35% of input gene IDs are fail to map...”
In [10]:
go <- enrichGO(gene     = ![本地路径](20200327_1.png "相对路径演示"),
              OrgDb        = org.Hs.eg.db,
              keyType       = 'ENTREZID',
              ont          = "BP",
              minGSSize    = 0,
              #maxGSSize    = 500,
              pvalueCutoff = 1,
              qvalueCutoff = 1,
              #eps = eps,
              #verbose      = FALSE
              )
In [11]:
go@result
A data.frame: 2029 × 9
IDDescriptionGeneRatioBgRatiopvaluep.adjustqvaluegeneIDCount
<chr><chr><chr><chr><dbl><dbl><dbl><chr><int>
GO:0000280GO:0000280nuclear division 11/58441/188708.497327e-080.00017241080.00011225427314/27338/5902/983/891/4605/811/1063/7153/10403/1005111
GO:0048285GO:0048285organelle fission 11/58488/188702.349718e-070.00023837880.00015520507314/27338/5902/983/891/4605/811/1063/7153/10403/1005111
GO:0009410GO:0009410response to xenobiotic stimulus 10/58434/188707.265422e-070.00037309890.0002429193595/2947/1728/8061/983/218/5111/1499/811/1063 10
GO:0045787GO:0045787positive regulation of cell cycle 9/58 334/188707.603798e-070.00037309890.0002429193595/8061/7027/5902/983/891/811/10403/10051 9
GO:0048144GO:0048144fibroblast proliferation 6/58 107/188709.194157e-070.00037309890.00024291932335/5052/1163/983/891/1499 6
GO:0140014GO:0140014mitotic nuclear division 8/58 274/188701.824414e-060.00061695600.000401691127338/5902/983/891/4605/1063/10403/10051 8
GO:0000302GO:0000302response to reactive oxygen species 7/58 205/188703.031463e-060.00087869120.00057210325052/1728/8061/8878/10105/983/5111 7
GO:0000819GO:0000819sister chromatid segregation 7/58 225/188705.598675e-060.00141996390.0009245180983/891/4605/1063/7153/10403/10051 7
GO:0034614GO:0034614cellular response to reactive oxygen species 6/58 154/188707.642157e-060.00160922530.00104774345052/1728/8878/10105/983/5111 6
GO:0044839GO:0044839cell cycle G2/M phase transition 6/58 155/188707.931125e-060.00160922530.0010477434595/983/891/2305/1063/10403 6
GO:0033044GO:0033044regulation of chromosome organization 7/58 247/188701.029670e-050.00178842380.0011644169983/891/1499/1063/7153/10403/10051 7
GO:0007292GO:0007292female gamete generation 6/58 163/188701.057717e-050.00178842380.00116441694192/7314/8061/1499/7153/10403 6
GO:0044772GO:0044772mitotic cell cycle phase transition 9/58 470/188701.231576e-050.00191331040.0012457288595/27338/7027/1163/983/891/2305/1063/10403 9
GO:0010038GO:0010038response to metal ion 8/58 359/188701.327532e-050.00191331040.00124572881728/4502/3727/7846/10105/983/5111/811 8
GO:0042542GO:0042542response to hydrogen peroxide 5/58 101/188701.454701e-050.00191331040.00124572881728/8061/10105/983/5111 5
GO:0090068GO:0090068positive regulation of cell cycle process 7/58 262/188701.508771e-050.00191331040.0012457288595/7027/5902/983/891/10403/10051 7
GO:0000070GO:0000070mitotic sister chromatid segregation 6/58 184/188702.105023e-050.00241962700.0015753843983/891/4605/1063/10403/10051 6
GO:0051402GO:0051402neuron apoptotic process 7/58 278/188702.209923e-050.00241962700.0015753843595/4192/1728/7314/7846/4605/1499 7
GO:0033047GO:0033047regulation of mitotic sister chromatid segregation 4/58 54/18870 2.265792e-050.00241962700.0015753843983/891/1063/10403 4
GO:0031099GO:0031099regeneration 6/58 191/188702.598302e-050.00263597710.0017162466595/928/4192/975/983/5111 6
GO:0071248GO:0071248cellular response to metal ion 6/58 200/188703.365097e-050.00324982730.00211591571728/4502/3727/7846/10105/811 6
GO:0051383GO:0051383kinetochore organization 3/58 21/18870 3.523716e-050.00324982730.00211591571063/10403/10051 3
GO:0007059GO:0007059chromosome segregation 8/58 424/188704.360081e-050.00384635000.002504303055839/983/891/4605/1063/7153/10403/10051 8
GO:0098813GO:0098813nuclear chromosome segregation 7/58 312/188704.610380e-050.00389769220.0025377312983/891/4605/1063/7153/10403/10051 7
GO:0051983GO:0051983regulation of chromosome segregation 5/58 131/188705.095420e-050.00413544310.0026925273983/891/1063/10403/10051 5
GO:0070301GO:0070301cellular response to hydrogen peroxide 4/58 67/18870 5.330706e-050.00416000100.00270851671728/10105/983/5111 4
GO:1905448GO:1905448positive regulation of mitochondrial ATP synthesis coupled electron transport2/58 4/18870 5.548980e-050.00416995560.0027149980983/891 2
GO:0007098GO:0007098centrosome cycle 5/58 138/188706.528838e-050.00469977100.00305995317846/5902/983/1499/10403 5
GO:0000086GO:0000086G2/M transition of mitotic cell cycle 5/58 140/188706.990543e-050.00469977100.0030599531595/983/891/2305/1063 5
GO:0071241GO:0071241cellular response to inorganic substance 6/58 229/188707.149451e-050.00469977100.00305995311728/4502/3727/7846/10105/811 6
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
GO:0043010GO:0043010camera-type eye development 1/58344/188700.65655420.66607430.43367131499 1
GO:0034249GO:0034249negative regulation of amide metabolic process 1/58345/188700.65762950.66683170.4341645811 1
GO:0071496GO:0071496cellular response to external stimulus 1/58346/188700.65870140.66758500.43465498061 1
GO:0032496GO:0032496response to lipopolysaccharide 1/58348/188700.66083540.66941340.43584541728 1
GO:0045862GO:0045862positive regulation of proteolysis 1/58350/188700.66295620.67089190.4368080302 1
GO:0051235GO:0051235maintenance of location 1/58350/188700.66295620.67089190.4368080811 1
GO:0070588GO:0070588calcium ion transmembrane transport 1/58352/188700.66506400.67268940.4379784302 1
GO:0030522GO:0030522intracellular receptor signaling pathway 1/58353/188700.66611310.67341480.4384507811 1
GO:0090287GO:0090287regulation of cellular response to growth factor stimulus 1/58357/188700.67027710.67728700.44097181499 1
GO:0009100GO:0009100glycoprotein metabolic process 1/58370/188700.68346070.68992130.44919781499 1
GO:0032535GO:0032535regulation of cellular component size 1/58370/188700.68346070.68992130.44919782335 1
GO:1901653GO:1901653cellular response to peptide 1/58374/188700.68741200.69356490.45157015315 1
GO:0002460GO:0002460adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains1/58380/188700.69324820.69910560.4551776975 1
GO:0001654GO:0001654eye development 1/58391/188700.70367100.70926400.46179161499 1
GO:0150063GO:0150063visual system development 1/58395/188700.70737420.71264260.46399131499 1
GO:0031331GO:0031331positive regulation of cellular catabolic process 1/58396/188700.70829280.71321400.46436338878 1
GO:0010959GO:0010959regulation of metal ion transport 1/58398/188700.71012170.71470080.46533141499 1
GO:0048880GO:0048880sensory system development 1/58401/188700.71284380.71708480.46688361499 1
GO:0003002GO:0003002regionalization 1/58430/188700.73789580.74155060.48281291499 1
GO:0043434GO:0043434response to peptide hormone 1/58430/188700.73789580.74155060.48281295315 1
GO:0042391GO:0042391regulation of membrane potential 1/58440/188700.74602610.74935000.48789107314 1
GO:0007409GO:0007409axonogenesis 1/58448/188700.75235140.75532960.49178422335 1
GO:0006869GO:0006869lipid transport 1/58453/188700.75622590.75884390.4940723302 1
GO:0034765GO:0034765regulation of monoatomic ion transmembrane transport 1/58454/188700.75699360.75923870.4943294101051
GO:0007015GO:0007015actin filament organization 1/58464/188700.76454120.76642990.4990114800 1
GO:0007389GO:0007389pattern specification process 1/58475/188700.77257760.77410360.50400771499 1
GO:0023061GO:0023061signal release 1/58484/188700.77895180.78010520.50791527052 1
GO:0050804GO:0050804modulation of chemical synaptic transmission 1/58489/188700.78241680.78318880.50992298878 1
GO:0099177GO:0099177regulation of trans-synaptic signaling 1/58490/188700.78310330.78348950.51011878878 1
GO:0031667GO:0031667response to nutrient levels 1/58495/188700.78650440.78650440.51208171728 1
In [33]:
ggo <- groupGO(gene     = gene$ENTREZID,
               OrgDb    = org.Hs.eg.db,
               keyType       = 'ENTREZID',
               ont      = "BP",
               #level    = 3,
               readable = TRUE)
In [34]:
ggo@result
A data.frame: 23 × 5
IDDescriptionCountGeneRatiogeneID
<chr><chr><int><chr><chr>
GO:0000003GO:0000003reproduction 2929/59 KRT8/KRT8/CD9/CD9/CD9/MDK/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/CDK1/CDK1/CDK1/CDK1/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/TOP2A/NDC80/NDC80/SMC4/SMC4/SMC4/SMC4
GO:0002376GO:0002376immune system process 3333/59 FN1/FN1/FN1/FN1/FN1/FN1/CD9/CD9/CD9/MDK/PRDX1/NQO1/NQO1/FOSL1/FOSL1/FOSL1/FOSL1/SQSTM1/CD24/CD24/CD24/CD81/CD81/CD81/LAMP1/LAMP1/PPP1R14B/TUBB4B/CTNNB1/CTNNB1/CTNNB1/CALR/CALR
GO:0008152GO:0008152metabolic process 100100/59FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/GSTM3/GSTM3/MDK/PRDX1/PGK1/PGK1/NQO1/NQO1/ALPP/ALPP/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/CTSA/CTSA/CTSA/CTSA/UBE2S/HSPB1/HSPB1/TCOF1/TCOF1/TCOF1/TFDP1/TFDP1/CSTB/CSTB/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CKS1B/CKS1B/CD24/CD24/CD24/LDHB/LDHB/TUBA1A/HMGA1/HMGA1/CD81/CD81/CD81/PPIF/PPIF/PSMB6/PSMB6/PSMB6/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/FOXM1/FOXM1/FOXM1/FOXM1/PPP1R14B/ALDH3A1/ALDH3A1/ALDH3A1/IGFBP3/IGFBP3/MYBL2/PHGDH/UBC/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/MCM3/MCM3/MCM3/CENPF/ANXA2/ANXA2/TOP2A
GO:0009987GO:0009987cellular process 120120/59FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/KRT8/KRT8/GSTM3/GSTM3/CD9/CD9/CD9/MDK/PRDX1/PGK1/PGK1/NQO1/NQO1/ALPP/ALPP/MT2A/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/CTSA/CTSA/CTSA/CTSA/UBE2S/HSPB1/HSPB1/MYL6/TCOF1/TCOF1/TCOF1/TFDP1/TFDP1/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CKS1B/CKS1B/CD24/CD24/CD24/LDHB/LDHB/TUBA1A/RANBP1/RANBP1/RANBP1/RANBP1/RANBP1/CENPN/HMGA1/HMGA1/CD81/CD81/CD81/PPIF/PPIF/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/FOXM1/FOXM1/FOXM1/FOXM1/LAMP1/LAMP1/PPP1R14B/TFPI2/ALDH3A1/ALDH3A1/ALDH3A1/CALD1/CALD1/TUBA4A/IGFBP3/IGFBP3/TUBB4B/MYBL2/PHGDH/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/MCM3/MCM3/MCM3/CENPF/ANXA2/ANXA2/TOP2A/NDC80/NDC80/SMC4/SMC4/SMC4/SMC4
GO:0016032GO:0016032viral process 1010/59 CD81/CD81/CD81/CDK1/CDK1/CDK1/CDK1/LAMP1/LAMP1/TOP2A
GO:0022414GO:0022414reproductive process 2929/59 KRT8/KRT8/CD9/CD9/CD9/MDK/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/CDK1/CDK1/CDK1/CDK1/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/TOP2A/NDC80/NDC80/SMC4/SMC4/SMC4/SMC4
GO:0032501GO:0032501multicellular organismal process 7878/59 FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/KRT8/KRT8/GSTM3/GSTM3/CD9/CD9/CD9/MDK/PRDX1/PGK1/PGK1/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/HSPB1/HSPB1/MYL6/TCOF1/TCOF1/TCOF1/CSTB/CSTB/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CD24/CD24/CD24/TUBA1A/CD81/CD81/CD81/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/LAMP1/LAMP1/TFPI2/CALD1/CALD1/IGFBP3/IGFBP3/PHGDH/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/CENPF/ANXA2/ANXA2/TOP2A/NDC80/NDC80
GO:0032502GO:0032502developmental process 7373/59 FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/KRT8/KRT8/GSTM3/GSTM3/CD9/CD9/CD9/MDK/PRDX1/PGK1/PGK1/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/HSPB1/HSPB1/MYL6/TCOF1/TCOF1/TCOF1/TFDP1/TFDP1/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CD24/CD24/CD24/TUBA1A/CD81/CD81/CD81/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/CALD1/CALD1/IGFBP3/IGFBP3/PHGDH/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/CENPF/ANXA2/ANXA2/TOP2A
GO:0040007GO:0040007growth 2323/59 FN1/FN1/FN1/FN1/FN1/FN1/CD9/CD9/CD9/MDK/MT2A/CD81/CD81/CD81/CDK1/CDK1/CDK1/CDK1/IGFBP3/IGFBP3/CTNNB1/CTNNB1/CTNNB1
GO:0040011GO:0040011locomotion 2323/59 FN1/FN1/FN1/FN1/FN1/FN1/CD9/CD9/CD9/MDK/FOSL1/FOSL1/FOSL1/FOSL1/HSPB1/HSPB1/CD81/CD81/CD81/IGFBP3/IGFBP3/CALR/CALR
GO:0042592GO:0042592homeostatic process 2323/59 PRDX1/NQO1/NQO1/MT2A/UBB/UBB/UBE2S/HSPB1/HSPB1/TGM2/TGM2/TGM2/TGM2/SQSTM1/CD24/CD24/CD24/TUBA1A/CTNNB1/CTNNB1/CTNNB1/CALR/CALR
GO:0043473GO:0043473pigmentation 00/59
GO:0044419GO:0044419biological process involved in interspecies interaction between organisms 3333/59 FN1/FN1/FN1/FN1/FN1/FN1/KRT8/KRT8/PRDX1/NQO1/NQO1/FOSL1/FOSL1/FOSL1/FOSL1/HSPB1/HSPB1/CD24/CD24/CD24/CD81/CD81/CD81/CDK1/CDK1/CDK1/CDK1/LAMP1/LAMP1/PPP1R14B/TUBB4B/CALR/CALR
GO:0044848GO:0044848biological phase 33/59 CTNNB1/CTNNB1/CTNNB1
GO:0048511GO:0048511rhythmic process 77/59 MDK/CDK1/CDK1/CDK1/CDK1/PCNA/TOP2A
GO:0048518GO:0048518positive regulation of biological process 7474/59 FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/MDK/NQO1/NQO1/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/UBE2S/HSPB1/HSPB1/TFDP1/TFDP1/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CD24/CD24/CD24/RANBP1/RANBP1/RANBP1/RANBP1/RANBP1/HMGA1/HMGA1/CD81/CD81/CD81/CDK1/CDK1/CDK1/CDK1/CCNB1/FOXM1/FOXM1/FOXM1/FOXM1/LAMP1/LAMP1/IGFBP3/IGFBP3/MYBL2/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/ANXA2/ANXA2/TOP2A/NDC80/NDC80/SMC4/SMC4/SMC4/SMC4
GO:0048519GO:0048519negative regulation of biological process 6666/59 FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/CD9/CD9/CD9/MDK/PGK1/PGK1/NQO1/NQO1/MT2A/FOSL1/FOSL1/FOSL1/FOSL1/CTSA/CTSA/CTSA/CTSA/HSPB1/HSPB1/TFDP1/TFDP1/CSTB/CSTB/JUND/SQSTM1/CD24/CD24/CD24/HMGA1/HMGA1/PPIF/PPIF/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/FOXM1/FOXM1/FOXM1/FOXM1/IGFBP3/IGFBP3/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/CENPF/ANXA2/ANXA2/TOP2A/NDC80/NDC80
GO:0050789GO:0050789regulation of biological process 106106/59FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/KRT8/KRT8/CD9/CD9/CD9/MDK/PRDX1/PGK1/PGK1/NQO1/NQO1/MT2A/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/CTSA/CTSA/CTSA/CTSA/UBE2S/HSPB1/HSPB1/TCOF1/TCOF1/TCOF1/TFDP1/TFDP1/CSTB/CSTB/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CKS1B/CKS1B/CD24/CD24/CD24/TUBA1A/RANBP1/RANBP1/RANBP1/RANBP1/RANBP1/HMGA1/HMGA1/CD81/CD81/CD81/PPIF/PPIF/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/FOXM1/FOXM1/FOXM1/FOXM1/LAMP1/LAMP1/PPP1R14B/IGFBP3/IGFBP3/MYBL2/PHGDH/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/MCM3/MCM3/MCM3/CENPF/ANXA2/ANXA2/TOP2A/NDC80/NDC80/SMC4/SMC4/SMC4/SMC4
GO:0050896GO:0050896response to stimulus 9494/59 FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/KRT8/KRT8/GSTM3/GSTM3/CD9/CD9/CD9/MDK/PRDX1/PGK1/PGK1/NQO1/NQO1/MT2A/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/HSPB1/HSPB1/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CD24/CD24/CD24/TUBA1A/RANBP1/RANBP1/RANBP1/RANBP1/RANBP1/HMGA1/HMGA1/CD81/CD81/CD81/PPIF/PPIF/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/FOXM1/FOXM1/FOXM1/FOXM1/LAMP1/LAMP1/PPP1R14B/TFPI2/ALDH3A1/ALDH3A1/ALDH3A1/IGFBP3/IGFBP3/TUBB4B/MYBL2/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/MCM3/MCM3/MCM3/CENPF/ANXA2/ANXA2/TOP2A/NDC80/NDC80
GO:0051179GO:0051179localization 4949/59 CCND1/CCND1/CD9/CD9/CD9/UBB/UBB/CTSA/CTSA/CTSA/CTSA/HSPB1/HSPB1/TGM2/TGM2/TGM2/TGM2/SQSTM1/CD24/CD24/CD24/TUBA1A/RANBP1/RANBP1/RANBP1/RANBP1/RANBP1/CD81/CD81/CD81/PPIF/PPIF/CDK1/CDK1/CDK1/CDK1/CCNB1/LAMP1/LAMP1/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/CENPF/ANXA2/ANXA2/NDC80/NDC80
GO:0051703GO:0051703biological process involved in intraspecies interaction between organisms 00/59
GO:0065007GO:0065007biological regulation 107107/59FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/KRT8/KRT8/CD9/CD9/CD9/MDK/PRDX1/PGK1/PGK1/NQO1/NQO1/MT2A/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/CTSA/CTSA/CTSA/CTSA/UBE2S/HSPB1/HSPB1/TCOF1/TCOF1/TCOF1/TFDP1/TFDP1/CSTB/CSTB/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CKS1B/CKS1B/CD24/CD24/CD24/TUBA1A/RANBP1/RANBP1/RANBP1/RANBP1/RANBP1/HMGA1/HMGA1/CD81/CD81/CD81/PPIF/PPIF/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/FOXM1/FOXM1/FOXM1/FOXM1/LAMP1/LAMP1/PPP1R14B/TFPI2/IGFBP3/IGFBP3/MYBL2/PHGDH/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/MCM3/MCM3/MCM3/CENPF/ANXA2/ANXA2/TOP2A/NDC80/NDC80/SMC4/SMC4/SMC4/SMC4
GO:0098754GO:0098754detoxification 66/59 GSTM3/GSTM3/PRDX1/NQO1/NQO1/MT2A
In [18]:
drivers_ann <- merge(x=gene,y=drivers,by.x = 'SYMBOL',by.y = 'V1',all = F)
In [28]:
weight <- drivers_ann$weight_shap_total_mean
names(weight) <- drivers_ann$ENTREZID
weight <- weight[order(weight,decreasing = T)]
In [31]:
gsego <- gseGO(gene     = weight,
              OrgDb        = org.Hs.eg.db,
              keyType       = 'ENTREZID',
              ont          = "BP",
              minGSSize    = 0,
              #maxGSSize    = 500,
              pvalueCutoff = 1,
              #qvalueCutoff = 1,
              #eps = eps,
              #verbose      = FALSE
              )
preparing geneSet collections...

GSEA analysis...

Warning message in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
“There are ties in the preranked stats (53.91% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.”
Warning message in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
“There are duplicate gene names, fgsea may produce unexpected results.”
Warning message in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
“All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".”
Warning message in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, :
“There were 1 pathways for which P-values were not calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)”
leading edge analysis...

done...

In [32]:
gsego@result
A data.frame: 2489 × 11
IDDescriptionsetSizeenrichmentScoreNESpvaluep.adjustqvaluerankleading_edgecore_enrichment
<chr><chr><int><dbl><dbl><dbl><dbl><dbl><dbl><chr><chr>
GO:0000819GO:0000819sister chromatid segregation 7-0.7685950-1.9070890.0006954240.30741440.307414436tags=100%, list=28%, signal=81%983/983/983/983/7153/4605/1063/10403/10403/10051/10051/10051/10051
GO:0033044GO:0033044regulation of chromosome organization 7-0.7685950-1.9070890.0006954240.30741440.307414436tags=100%, list=28%, signal=82%983/983/983/983/7153/1063/10403/10403/10051/10051/10051/10051/1499/1499/1499
GO:0098813GO:0098813nuclear chromosome segregation 7-0.7685950-1.9070890.0006954240.30741440.307414436tags=100%, list=28%, signal=81%983/983/983/983/7153/4605/1063/10403/10403/10051/10051/10051/10051
GO:0000070GO:0000070mitotic sister chromatid segregation 6-0.7622951-1.8586720.0027651560.30741440.307414436tags=100%, list=28%, signal=80%983/983/983/983/4605/1063/10403/10403/10051/10051/10051/10051
GO:0051276GO:0051276chromosome organization 12-0.5603448-1.7866870.0049173570.30741440.307414464tags=96%, list=50%, signal=59% 3159/3159/891/983/983/983/983/5111/7153/4605/1063/10403/10403/10051/10051/10051/10051/4172/4172/4172/1499/1499/1499
GO:0051983GO:0051983regulation of chromosome segregation 5-0.7560976-1.7498910.0071034530.30741440.307414436tags=100%, list=28%, signal=79%983/983/983/983/1063/10403/10403/10051/10051/10051/10051
GO:0051383GO:0051383kinetochore organization 3-0.8960000-1.6852440.0062603190.30741440.307414417tags=100%, list=13%, signal=92%10403/10403/10051/10051/10051/10051
GO:0006281GO:0006281DNA repair 4-0.7580645-1.6319760.0361725830.30741440.307414435tags=100%, list=27%, signal=80%983/983/983/2305/2305/2305/2305/5111/4172/4172/4172
GO:0010638GO:0010638positive regulation of organelle organization 4-0.7580645-1.6319760.0361725830.30741440.307414435tags=100%, list=27%, signal=81%983/983/983/10051/10051/10051/10051/302/302/1499/1499/1499
GO:0051054GO:0051054positive regulation of DNA metabolic process 4-0.7580645-1.6319760.0361725830.30741440.307414435tags=100%, list=27%, signal=80%983/983/983/2305/2305/2305/2305/5111/1499/1499/1499
GO:0033045GO:0033045regulation of sister chromatid segregation 4-0.7500000-1.6146150.0411906930.30741440.307414436tags=100%, list=28%, signal=77%983/983/983/983/1063/10403/10403
GO:0033047GO:0033047regulation of mitotic sister chromatid segregation 4-0.7500000-1.6146150.0411906930.30741440.307414436tags=100%, list=28%, signal=77%983/983/983/983/1063/10403/10403
GO:0051304GO:0051304chromosome separation 4-0.7500000-1.6146150.0411906930.30741440.307414436tags=88%, list=28%, signal=67% 1063/10403/10403/10051/10051/10051/10051
GO:0051783GO:0051783regulation of nuclear division 4-0.7500000-1.6146150.0411906930.30741440.307414436tags=83%, list=28%, signal=63% 811/811/1063/10403/10403
GO:1902850GO:1902850microtubule cytoskeleton organization involved in mitosis 4-0.7500000-1.6146150.0411906930.30741440.307414436tags=100%, list=28%, signal=77%983/983/983/983/4605/10403/10403
GO:1905818GO:1905818regulation of chromosome separation 4-0.7500000-1.6146150.0411906930.30741440.307414436tags=88%, list=28%, signal=67% 1063/10403/10403/10051/10051/10051/10051
GO:2001251GO:2001251negative regulation of chromosome organization 4-0.7500000-1.6146150.0411906930.30741440.307414436tags=100%, list=28%, signal=75%7153/1063/10403/10403
GO:0060537GO:0060537muscle tissue development 5 0.8577866 1.5924430.0059395210.30741440.3074144 1tags=9%, list=1%, signal=10% 4637
GO:0007517GO:0007517muscle organ development 3 0.9576972 1.5901360.0014017710.30741440.3074144 1tags=20%, list=1%, signal=21% 4637
GO:0033002GO:0033002muscle cell proliferation 4-0.7338710-1.5798920.0550523470.30741440.307414438tags=100%, list=30%, signal=77%3486/983/983/983/983/10403/10403/1499/1499/1499
GO:0051129GO:0051129negative regulation of cellular component organization 6-0.6475410-1.5788720.0191731900.30741440.307414450tags=100%, list=39%, signal=66%10105/891/7153/1063/10403/10403/302/302
GO:0050767GO:0050767regulation of neurogenesis 5 0.8441406 1.5671100.0078196280.30741440.307414420tags=69%, list=16%, signal=66% 2335/2335/2335/2335/2335/2335/7052/7052/7052/7052/4192
GO:0051960GO:0051960regulation of nervous system development 5 0.8441406 1.5671100.0078196280.30741440.307414420tags=69%, list=16%, signal=66% 2335/2335/2335/2335/2335/2335/7052/7052/7052/7052/4192
GO:0060284GO:0060284regulation of cell development 5 0.8441406 1.5671100.0078196280.30741440.307414420tags=69%, list=16%, signal=66% 2335/2335/2335/2335/2335/2335/7052/7052/7052/7052/4192
GO:0097237GO:0097237cellular response to toxic substance 3 0.9280000 1.5408280.0077920790.30741440.307414412tags=100%, list=9%, signal=94% 1728/1728/2947/2947/5052
GO:1990748GO:1990748cellular detoxification 3 0.9280000 1.5408280.0077920790.30741440.307414412tags=100%, list=9%, signal=94% 1728/1728/2947/2947/5052
GO:0006816GO:0006816calcium ion transport 2-0.9523810-1.5382810.0114605540.30741440.3074144 9tags=100%, list=7%, signal=97% 302/1499/1499/1499
GO:0030001GO:0030001metal ion transport 2-0.9523810-1.5382810.0114605540.30741440.3074144 9tags=100%, list=7%, signal=97% 302/1499/1499/1499
GO:0007507GO:0007507heart development 5 0.8231498 1.5281410.0141706180.30741440.3074144 7tags=38%, list=5%, signal=41% 2335/2335/2335/2335/2335/2335
GO:0010720GO:0010720positive regulation of cell development 4 0.8552063 1.5024330.0160487780.30741440.307414420tags=79%, list=16%, signal=74% 2335/2335/2335/2335/2335/2335/7052/7052/7052/7052/4192
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
GO:0051052GO:0051052regulation of DNA metabolic process 6 0.3126659 0.59945050.95950510.99075720.990757294tags=88%, list=73%, signal=27% 983/983/983/983/2305/2305/2305/2305/5111/4172/4172/4172/1499/1499/1499
GO:0097435GO:0097435supramolecular fiber organization 5 0.3188595 0.59194850.96900110.99075720.990757229tags=20%, list=23%, signal=17% 3856/3856
GO:0031323GO:0031323regulation of cellular metabolic process 30 0.2383703 0.58245530.97902100.99075720.990757265tags=51%, list=51%, signal=54% 2335/2335/2335/2335/2335/2335/4192/8061/8061/8061/8061/5476/5476/5476/5476/7027/7027/8878/100133941/100133941/100133941/975/975/975/1163/1163/5315/5315/5315/5315/595/595/6949/6949/6949
GO:0009968GO:0009968negative regulation of signal transduction 7 0.2924752 0.58240150.96956520.99075720.990757261tags=94%, list=48%, signal=56% 3315/3315/10105/10105/3486/3486/2305/2305/2305/2305/811/811/1499/1499/1499
GO:0010648GO:0010648negative regulation of cell communication 7 0.2924752 0.58240150.96956520.99075720.990757261tags=94%, list=48%, signal=56% 3315/3315/10105/10105/3486/3486/2305/2305/2305/2305/811/811/1499/1499/1499
GO:0023057GO:0023057negative regulation of signaling 7 0.2924752 0.58240150.96956520.99075720.990757261tags=94%, list=48%, signal=56% 3315/3315/10105/10105/3486/3486/2305/2305/2305/2305/811/811/1499/1499/1499
GO:0051172GO:0051172negative regulation of nitrogen compound metabolic process16 0.2548207 0.58141030.96960490.99075720.990757243tags=26%, list=34%, signal=24% 1728/1728/5476/5476/5476/5476/1476/1476/8878
GO:0051726GO:0051726regulation of cell cycle 14 0.2562795 0.57454640.96751270.99075720.990757262tags=41%, list=48%, signal=29% 8061/8061/8061/8061/7027/7027/5902/5902/5902/5902/5902/1163/1163/595/595
GO:0031324GO:0031324negative regulation of cellular metabolic process 17 0.2572748 0.59124860.98270600.99106780.9910678 7tags=14%, list=5%, signal=20% 2335/2335/2335/2335/2335/2335
GO:0006360GO:0006360transcription by RNA polymerase I 1 0.5118110 0.68273300.98605580.99243540.992435465tags=100%, list=51%, signal=50%6949/6949/6949
GO:0009303GO:0009303rRNA transcription 1 0.5118110 0.68273300.98605580.99243540.992435465tags=100%, list=51%, signal=50%6949/6949/6949
GO:0014029GO:0014029neural crest formation 1 0.5118110 0.68273300.98605580.99243540.992435465tags=100%, list=51%, signal=50%6949/6949/6949
GO:0016072GO:0016072rRNA metabolic process 1 0.5118110 0.68273300.98605580.99243540.992435465tags=100%, list=51%, signal=50%6949/6949/6949
GO:0042790GO:0042790nucleolar large rRNA transcription by RNA polymerase I 1 0.5118110 0.68273300.98605580.99243540.992435465tags=100%, list=51%, signal=50%6949/6949/6949
GO:0007005GO:0007005mitochondrion organization 2 0.3809524 0.57669250.98816570.99362300.993623081tags=100%, list=63%, signal=38%8878/10105/10105
GO:0010821GO:0010821regulation of mitochondrion organization 2 0.3809524 0.57669250.98816570.99362300.993623081tags=100%, list=63%, signal=38%8878/10105/10105
GO:0006357GO:0006357regulation of transcription by RNA polymerase II 15 0.2273327 0.51341390.98883250.99362300.993623062tags=36%, list=48%, signal=25% 8061/8061/8061/8061/7027/7027/8878/975/975/975/595/595
GO:0006366GO:0006366transcription by RNA polymerase II 15 0.2273327 0.51341390.98883250.99362300.993623062tags=36%, list=48%, signal=25% 8061/8061/8061/8061/7027/7027/8878/975/975/975/595/595
GO:0031055GO:0031055chromatin remodeling at centromere 1-0.5118110-0.67133750.99000000.99389690.993896964tags=100%, list=50%, signal=50%
GO:0034080GO:0034080CENP-A containing chromatin assembly 1-0.5118110-0.67133750.99000000.99389690.993896964tags=100%, list=50%, signal=50%
GO:0044092GO:0044092negative regulation of molecular function 4 0.3145161 0.55254430.99030300.99389690.993896991tags=100%, list=71%, signal=31%1476/1476/3315/3315/10105/10105/871/871/871
GO:0043086GO:0043086negative regulation of catalytic activity 3 0.3120000 0.51803700.99609880.99739770.997397791tags=100%, list=71%, signal=31%1476/1476/3315/3315/871/871/871
GO:0045931GO:0045931positive regulation of mitotic cell cycle 4 0.2873663 0.50484720.99636360.99739770.997397794tags=100%, list=73%, signal=29%7027/595/595/891/983/983/983/983
GO:1901989GO:1901989positive regulation of cell cycle phase transition 4 0.2873663 0.50484720.99636360.99739770.997397794tags=100%, list=73%, signal=29%7027/595/595/891/983/983/983/983
GO:1901992GO:1901992positive regulation of mitotic cell cycle phase transition 4 0.2873663 0.50484720.99636360.99739770.997397794tags=100%, list=73%, signal=29%7027/595/595/891/983/983/983/983
GO:0000280GO:0000280nuclear division 11 0.2212110 0.47904550.99483470.99739770.997397768tags=33%, list=53%, signal=19% 27338/5902/5902/5902/5902/5902/7314/7314
GO:0048285GO:0048285organelle fission 11 0.2212110 0.47904550.99483470.99739770.997397768tags=33%, list=53%, signal=19% 27338/5902/5902/5902/5902/5902/7314/7314
GO:0007049GO:0007049cell cycle 25 0.2000446 0.47782600.99699700.99739770.997397774tags=41%, list=58%, signal=29% 27338/8061/8061/8061/8061/7027/7027/5902/5902/5902/5902/5902/1163/1163/595/595/55839/7314/7314/3727/7846
GO:0000278GO:0000278mitotic cell cycle 18 0.1943552 0.44849350.99696660.99739770.997397762tags=31%, list=48%, signal=23% 27338/7027/7027/5902/5902/5902/5902/5902/1163/1163/595/595
GO:0022402GO:0022402cell cycle process 21 0.1944509 0.45593830.99899400.99899400.998994068tags=34%, list=53%, signal=24% 27338/7027/7027/5902/5902/5902/5902/5902/1163/1163/595/595/55839/7314/7314
In [41]:
drivers[drivers$is_tf]
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
FOSL10.00426576850.0005058976-9.429040e-0510TRUEFALSEFALSEFALSE
TFDP10.00333042770.0003013794-9.927206e-0510TRUEFALSEFALSEFALSE
JUND 0.00168432410.0003056773 1.057126e-0410TRUEFALSEFALSEFALSE
HMGA10.00125680320.0005449500-4.842230e-05 7TRUEFALSEFALSEFALSE
FOXM10.00066866760.0001551108-7.297643e-06 5TRUEFALSEFALSEFALSE
MYBL20.00024020410.0001968738 6.863686e-06 2TRUEFALSEFALSEFALSE
In [42]:
drivers[drivers$is_in_FAM]
A data.table: 2 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
H2AFZ 0.0014095510.00023773854.583457e-0510FALSETRUEFALSETRUE
ALDH3A10.0013055380.00019661694.354592e-07 4FALSETRUEFALSETRUE
In [43]:
drivers[drivers$is_in_ROS]
A data.table: 2 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
PRDX10.0057532930.00059483050.000130708310FALSEFALSETRUETRUE
NQO1 0.0071891720.00058341660.000174598810FALSEFALSETRUETRUE
In [45]:
drivers[drivers$is_in_Pathway]
A data.table: 4 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
H2AFZ 0.0014095510.00023773854.583457e-0510FALSE TRUEFALSETRUE
PRDX1 0.0057532930.00059483051.307083e-0410FALSEFALSE TRUETRUE
NQO1 0.0071891720.00058341661.745988e-0410FALSEFALSE TRUETRUE
ALDH3A10.0013055380.00019661694.354592e-07 4FALSE TRUEFALSETRUE
In [61]:
drivers$rank = 1:dim(drivers)[1]
In [62]:
drivers[drivers$is_in_Pathway]
A data.table: 4 × 10
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><int>
H2AFZ 0.0014095510.00023773854.583457e-0510FALSE TRUEFALSETRUE 7
PRDX1 0.0057532930.00059483051.307083e-0410FALSEFALSE TRUETRUE10
NQO1 0.0071891720.00058341661.745988e-0410FALSEFALSE TRUETRUE12
ALDH3A10.0013055380.00019661694.354592e-07 4FALSE TRUEFALSETRUE46
In [48]:
driver_info <- fread(file.path(read_dir,'driver_info_9.csv'))
In [49]:
driver_info[driver_info$is_in_Pathway]
A data.table: 3 × 23
V1is_tfis_in_FAMis_in_ROSis_in_Pathwayweight_shap_totalweight_shap_total_dirweight_shap_0weight_shap_0_dirweight_shap_1⋯weight_grad_0weight_grad_0_dirweight_grad_1weight_grad_1_diris_driver_shap_totalis_driver_shap_0is_driver_shap_1is_driver_grad_totalis_driver_grad_0is_driver_grad_1
<chr><lgl><lgl><lgl><lgl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><lgl><lgl><lgl><lgl><lgl><lgl>
H2AFZFALSE TRUEFALSETRUE0.001396736-4.011072e-050.001240849 0.0014747160.001561170⋯0.0013318746.733973e-110.00086870934.257443e-09TRUETRUETRUETRUETRUETRUE
PRDX1FALSEFALSE TRUETRUE0.005420250-4.785569e-040.005700868-0.0097415190.005124247⋯0.0025733741.301101e-100.00260049731.291586e-08TRUETRUETRUETRUETRUETRUE
NQO1 FALSEFALSE TRUETRUE0.007288831-1.938082e-040.007157244-0.0121872780.007427631⋯0.0032935661.665231e-100.00316861461.573753e-08TRUETRUETRUETRUETRUETRUE
In [209]:
kk <- enrichKEGG(gene         = gene$ENTREZID,
                 #keyType      = 'uniprot',
                 organism     = 'hsa',
                 pvalueCutoff = 0.1)
head(kk)
A data.frame: 6 × 11
categorysubcategoryIDDescriptionGeneRatioBgRatiopvaluep.adjustqvaluegeneIDCount
<chr><chr><chr><chr><chr><chr><dbl><dbl><dbl><chr><int>
hsa04218Cellular ProcessesCell growth and death hsa04218Cellular senescence 7/49156/87532.287756e-050.0017167760.001392998595/8878/983/891/2305/3486/4605 7
hsa04110Cellular ProcessesCell growth and death hsa04110Cell cycle 7/49157/87532.384412e-050.0017167760.001392998595/7027/983/891/5111/4172/10403 7
hsa00010Metabolism Carbohydrate metabolism hsa00010Glycolysis / Gluconeogenesis 4/4967/8753 5.127849e-040.0226630760.0183889005230/5315/3945/218 4
hsa05012Human Diseases Neurodegenerative diseasehsa05012Parkinson disease 7/49266/87536.370286e-040.0226630760.0183889007314/7846/10105/5694/7277/10383/7316 7
hsa04115Cellular ProcessesCell growth and death hsa04115p53 signaling pathway 4/4975/8753 7.869124e-040.0226630760.018388900595/983/891/3486 4
hsa05022Human Diseases Neurodegenerative diseasehsa05022Pathways of neurodegeneration - multiple diseases9/49476/87531.128896e-030.0270935160.0219837747314/8878/7846/10105/5694/7277/10383/7316/14999
In [208]:
?enrichKEGG
In [66]:
plot(table(kk@result$subcategory))
No description has been provided for this image
In [53]:
browseKEGG(kk, 'hsa05208')
Error in browseURL(url): 'browser' must be a non-empty character string
Traceback:

1. browseKEGG(kk, "hsa05208")
2. browseURL(url)
3. stop("'browser' must be a non-empty character string")
In [54]:
library("pathview")
##############################################################################
Pathview is an open source software package distributed under GNU General
Public License version 3 (GPLv3). Details of GPLv3 is available at
http://www.gnu.org/licenses/gpl-3.0.html. Particullary, users are required to
formally cite the original Pathview paper (not just mention it) in publications
or products. For details, do citation("pathview") within R.

The pathview downloads and uses KEGG data. Non-academic uses may require a KEGG
license agreement (details at http://www.kegg.jp/kegg/legal.html).
##############################################################################

In [56]:
hsa05208 <- pathview(gene.data  = weight,
                     pathway.id = "hsa05208",
                     species    = "hsa",
                     limit      = list(gene=max(abs(weight)), cpd=1))
Info: Downloading xml files for hsa05208, 1/1 pathways..

Info: Downloading png files for hsa05208, 1/1 pathways..

'select()' returned 1:1 mapping between keys and columns

Info: Working in directory /sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master

Info: Writing image file hsa05208.pathview.png

pathview

In [183]:
kk@result['hsa05208',]
A data.frame: 1 × 11
categorysubcategoryIDDescriptionGeneRatioBgRatiopvaluep.adjustqvaluegeneIDCount
<chr><chr><chr><chr><chr><chr><dbl><dbl><dbl><chr><int>
hsa05208Human DiseasesCancer: overviewhsa05208Chemical carcinogenesis - reactive oxygen species4/72331/110080.17073920.28154190.1926339P21266/Q6FGJ9/P15559/P304054
In [ ]:
bngeneplot(results = kk@result, exp = vsted, pathNum = 17)
In [190]:
set.seed(123)
counts <- matrix(sample(1:100, 100, replace=TRUE), nrow=10, ncol=10)
numbers <- 1:10

# 将数值向量转换为字符向量
char_vector <- as.character(numbers)
In [193]:
library(DESeq2)
#counts = read.table("GSE133624_reads-count-all-sample.txt", header=1, row.names=1)
meta = sapply(char_vector, function (x) substring(x,1,1))
meta = data.frame(meta)
colnames(meta) = c("Condition")

dds <- DESeqDataSetFromMatrix(countData = counts,
                              colData = meta,
                              design= ~ Condition)
v = varianceStabilizingTransformation(dds, blind=FALSE)
vsted = assay(v)
Warning message in DESeqDataSet(se, design = design, ignoreRank):
“some variables in design formula are characters, converting to factors”
-- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.

Warning message in lfproc(x, y, weights = weights, cens = cens, base = base, geth = geth, :
“Estimated rdf < 1.0; not estimating variance”
In [194]:
vsted
A matrix: 10 × 10 of type dbl
12345678910
4.7241335.4912944.5352674.4956364.4950665.5323224.5679985.9634935.8279684.496718
5.9603475.5130974.4950665.2323865.0402825.1355374.7182355.3199204.5158164.496024
5.6907464.9280365.2725394.4950664.4955384.4950665.8664354.8551985.9636295.291622
4.4950675.5130974.4950664.4950665.9318075.6576545.1917415.9630484.9411865.918016
5.9350604.6218865.9614745.7069134.5518025.8653375.9040005.7688844.5770914.495066
5.3174165.5314894.9577325.9635075.8951585.8020804.4950665.0420225.9635755.880892
5.6601094.4950665.9580954.6319644.5110304.4950745.8976954.4950665.1262505.901836
5.3674415.5496535.9604654.8853325.9190734.5081344.4950664.4950685.4034155.522206
4.4950675.6526595.3192155.9571935.5830024.4950675.5695315.9558274.8157385.390085
4.5285175.0153935.9556805.9333135.6525735.5971275.8238284.5684874.4950674.495071
In [222]:
mtx <- drivers[,c('weight_shap_total_mean')]
#rownames(mtx) <- drivers$V1
mtx <- as.matrix(mtx)

replicated_matrix <- matrix(mtx, nrow=nrow(mtx), ncol=10, byrow=FALSE)
rownames(replicated_matrix) <- drivers$V1
colnames(replicated_matrix) <- seq(1,10)
In [227]:
kk
#
# over-representation test
#
#...@organism 	 hsa 
#...@ontology 	 KEGG 
#...@keytype 	 kegg 
#...@gene 	 chr [1:59] "2335" "595" "3856" "2947" "928" "9741" "4192" "5052" "5230" ...
#...pvalues adjusted by 'BH' with cutoff <0.1 
#...11 enriched terms found
'data.frame':	11 obs. of  11 variables:
 $ category   : chr  "Cellular Processes" "Cellular Processes" "Metabolism" "Human Diseases" ...
 $ subcategory: chr  "Cell growth and death" "Cell growth and death" "Carbohydrate metabolism" "Neurodegenerative disease" ...
 $ ID         : chr  "hsa04218" "hsa04110" "hsa00010" "hsa05012" ...
 $ Description: chr  "Cellular senescence" "Cell cycle" "Glycolysis / Gluconeogenesis" "Parkinson disease" ...
 $ GeneRatio  : chr  "7/49" "7/49" "4/49" "7/49" ...
 $ BgRatio    : chr  "156/8753" "157/8753" "67/8753" "266/8753" ...
 $ pvalue     : num  2.29e-05 2.38e-05 5.13e-04 6.37e-04 7.87e-04 ...
 $ p.adjust   : num  0.00172 0.00172 0.02266 0.02266 0.02266 ...
 $ qvalue     : num  0.00139 0.00139 0.01839 0.01839 0.01839 ...
 $ geneID     : chr  "595/8878/983/891/2305/3486/4605" "595/7027/983/891/5111/4172/10403" "5230/5315/3945/218" "7314/7846/10105/5694/7277/10383/7316" ...
 $ Count      : int  7 7 4 7 4 9 4 5 5 6 ...
#...Citation
 T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu.
 clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.
 The Innovation. 2021, 2(3):100141 
In [211]:
pway = setReadable(kk, OrgDb=org.Hs.eg.db, keyType="ENTREZID")
In [231]:
ttt <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
rownames(ttt) <- ttt$SYMBOL
'select()' returned 1:1 mapping between keys and columns

Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“1.67% of input gene IDs are fail to map...”
In [236]:
rownames(replicated_matrix) <- ttt[rownames(replicated_matrix),'ENTREZID']
In [276]:
noise <- matrix(rnorm(25, mean=1, sd=1),nrow=nrow(replicated_matrix),ncol=ncol(replicated_matrix))

# 将随机噪声添加到原始矩阵
noisy_matrix <- replicated_matrix*1000 + noise

rownames(noisy_matrix) <- drivers$V1
colnames(noisy_matrix) <- seq(1,10)
rownames(noisy_matrix) <- ttt[rownames(noisy_matrix),'ENTREZID']
In [280]:
CBNplot::bngeneplot(results = pway, exp = noisy_matrix, expRow = 'ENTREZID',pathNum = 62)
'select()' returned 1:1 mapping between keys and columns

'select()' returned 1:1 mapping between keys and columns

no edge present in graph

'error'
In [264]:
which(kk@result$ID == 'hsa05208')
62
In [243]:
attributes(pway)$result
A data.frame: 144 × 11
categorysubcategoryIDDescriptionGeneRatioBgRatiopvaluep.adjustqvaluegeneIDCount
<chr><chr><chr><chr><chr><chr><dbl><dbl><dbl><chr><int>
hsa04218Cellular Processes Cell growth and death hsa04218Cellular senescence 7/49156/87532.287756e-050.0017167760.001392998CCND1/SQSTM1/CDK1/CCNB1/FOXM1/IGFBP3/MYBL2 7
hsa04110Cellular Processes Cell growth and death hsa04110Cell cycle 7/49157/87532.384412e-050.0017167760.001392998CCND1/TFDP1/CDK1/CCNB1/PCNA/MCM3/NDC80 7
hsa00010Metabolism Carbohydrate metabolism hsa00010Glycolysis / Gluconeogenesis 4/4967/8753 5.127849e-040.0226630760.018388900PGK1/PKM/LDHB/ALDH3A1 4
hsa05012Human Diseases Neurodegenerative disease hsa05012Parkinson disease 7/49266/87536.370286e-040.0226630760.018388900UBB/TUBA1A/PPIF/PSMB6/TUBA4A/TUBB4B/UBC 7
hsa04115Cellular Processes Cell growth and death hsa04115p53 signaling pathway 4/4975/8753 7.869124e-040.0226630760.018388900CCND1/CDK1/CCNB1/IGFBP3 4
hsa05022Human Diseases Neurodegenerative disease hsa05022Pathways of neurodegeneration - multiple diseases9/49476/87531.128896e-030.0270935160.021983774UBB/SQSTM1/TUBA1A/PPIF/PSMB6/TUBA4A/TUBB4B/UBC/CTNNB19
hsa04540Cellular Processes Cellular community - eukaryoteshsa04540Gap junction 4/4988/8753 1.431436e-030.0274220760.022250369TUBA1A/CDK1/TUBA4A/TUBB4B 4
hsa04145Cellular Processes Transport and catabolism hsa04145Phagosome 5/49152/87531.523449e-030.0274220760.022250369TUBA1A/LAMP1/TUBA4A/TUBB4B/CALR 5
hsa04530Cellular Processes Cellular community - eukaryoteshsa04530Tight junction 5/49170/87532.490451e-030.0398472190.032332174CCND1/MYL6/TUBA1A/TUBA4A/PCNA 5
hsa05016Human Diseases Neurodegenerative disease hsa05016Huntington disease 6/49306/87536.856213e-030.0971722990.078845944TGM2/TUBA1A/PPIF/PSMB6/TUBA4A/TUBB4B 6
hsa05418Human Diseases Cardiovascular disease hsa05418Fluid shear stress and atherosclerosis 4/49139/87537.422884e-030.0971722990.078845944GSTM3/NQO1/SQSTM1/CTNNB1 4
hsa01230Metabolism Global and overview maps hsa01230Biosynthesis of amino acids 3/4975/8753 8.393387e-030.1007206410.081725082PGK1/PKM/PHGDH 3
hsa05132Human Diseases Infectious disease: bacterial hsa05132Salmonella infection 5/49247/87531.191903e-020.1290473980.104709512TUBA1A/TUBA4A/TUBB4B/CTNNB1/ANXA2 5
hsa04140Cellular Processes Transport and catabolism hsa04140Autophagy - animal 4/49165/87531.335173e-020.1290473980.104709512UBB/SQSTM1/LAMP1/UBC 4
hsa05225Human Diseases Cancer: specific types hsa05225Hepatocellular carcinoma 4/49168/87531.418522e-020.1290473980.104709512CCND1/GSTM3/NQO1/CTNNB1 4
hsa05222Human Diseases Cancer: specific types hsa05222Small cell lung cancer 3/4992/8753 1.461035e-020.1290473980.104709512FN1/CCND1/CKS1B 3
hsa03030Genetic Information ProcessingReplication and repair hsa03030DNA replication 2/4936/8753 1.713450e-020.1290473980.104709512PCNA/MCM3 2
hsa05020Human Diseases Neurodegenerative disease hsa05020Prion disease 5/49272/87531.746331e-020.1290473980.104709512TUBA1A/PPIF/PSMB6/TUBA4A/TUBB4B 5
hsa05216Human Diseases Cancer: specific types hsa05216Thyroid cancer 2/4937/8753 1.804957e-020.1290473980.104709512CCND1/CTNNB1 2
hsa05146Human Diseases Infectious disease: parasitic hsa05146Amoebiasis 3/49102/87531.921568e-020.1290473980.104709512FN1/PRDX1/HSPB1 3
hsa05010Human Diseases Neurodegenerative disease hsa05010Alzheimer disease 6/49384/87531.947279e-020.1290473980.104709512TUBA1A/PPIF/PSMB6/TUBA4A/TUBB4B/CTNNB1 6
hsa04137Cellular Processes Transport and catabolism hsa04137Mitophagy - animal 3/49103/87531.971557e-020.1290473980.104709512UBB/SQSTM1/UBC 3
hsa04814Cellular Processes Cell motility hsa04814Motor proteins 4/49193/87532.243184e-020.1368696170.111056488MYL6/TUBA1A/TUBA4A/TUBB4B 4
hsa05167Human Diseases Infectious disease: viral hsa05167Kaposi sarcoma-associated herpesvirus infection 4/49194/87532.281160e-020.1368696170.111056488CCND1/UBB/UBC/CTNNB1 4
hsa01200Metabolism Global and overview maps hsa01200Carbon metabolism 3/49115/87532.627589e-020.1485817690.120559769PGK1/PKM/PHGDH 3
hsa05203Human Diseases Cancer: overview hsa05203Viral carcinogenesis 4/49204/87532.682726e-020.1485817690.120559769CCND1/PKM/RANBP1/CDK1 4
hsa00620Metabolism Carbohydrate metabolism hsa00620Pyruvate metabolism 2/4947/8753 2.827941e-020.1508235160.122378730PKM/LDHB 2
hsa00270Metabolism Amino acid metabolism hsa00270Cysteine and methionine metabolism 2/4952/8753 3.408291e-020.1741523030.141307790LDHB/PHGDH 2
hsa05166Human Diseases Infectious disease: viral hsa05166Human T-cell leukemia virus 1 infection 4/49222/87533.507234e-020.1741523030.141307790CCND1/FOSL1/RANBP1/CALR 4
hsa04142Cellular Processes Transport and catabolism hsa04142Lysosome 3/49132/87533.734000e-020.1792319890.145429464LAPTM4A/CTSA/LAMP1 3
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
hsa01522Human Diseases Drug resistance: antineoplastic hsa01522Endocrine resistance 1/4998/8753 0.42491250.53206440.4317189CCND1 1
hsa04916Organismal Systems Endocrine system hsa04916Melanogenesis 1/49101/87530.43462600.53886520.4372371CTNNB1 1
hsa05142Human Diseases Infectious disease: parasitic hsa05142Chagas disease 1/49102/87530.43782790.53886520.4372371CALR 1
hsa04350Environmental Information ProcessingSignal transduction hsa04350TGF-beta signaling pathway 1/49108/87530.45667000.55729210.4521888TFDP1 1
hsa05145Human Diseases Infectious disease: parasitic hsa05145Toxoplasmosis 1/49111/87530.46585760.56372680.4574099PPIF 1
hsa04928Organismal Systems Endocrine system hsa04928Parathyroid hormone synthesis, secretion and action 1/49114/87530.47489290.56870630.4614503JUND 1
hsa04670Organismal Systems Immune system hsa04670Leukocyte transendothelial migration 1/49115/87530.47787130.56870630.4614503CTNNB1 1
hsa04935Organismal Systems Endocrine system hsa04935Growth hormone synthesis, secretion and action 1/49120/87530.49251670.57997720.4705955IGFBP3 1
hsa04152Environmental Information ProcessingSignal transduction hsa04152AMPK signaling pathway 1/49121/87530.49539720.57997720.4705955CCND1 1
hsa04010Environmental Information ProcessingSignal transduction hsa04010MAPK signaling pathway 2/49299/87530.50283290.58393500.4738069HSPB1/JUND2
hsa05135Human Diseases Infectious disease: bacterial hsa05135Yersinia infection 1/49137/87530.53936380.61748620.5010305FN1 1
hsa05162Human Diseases Infectious disease: viral hsa05162Measles 1/49138/87530.54198350.61748620.5010305CCND1 1
hsa04371Environmental Information ProcessingSignal transduction hsa04371Apelin signaling pathway 1/49139/87530.54458860.61748620.5010305CCND1 1
hsa04550Cellular Processes Cellular community - eukaryotes hsa04550Signaling pathways regulating pluripotency of stem cells1/49143/87530.55486450.62422260.5064964CTNNB1 1
hsa04217Cellular Processes Cell growth and death hsa04217Necroptosis 1/49159/87530.59374470.65758240.5335647SQSTM1 1
hsa05161Human Diseases Infectious disease: viral hsa05161Hepatitis B 1/49162/87530.60065490.65758240.5335647PCNA 1
hsa04151Environmental Information ProcessingSignal transduction hsa04151PI3K-Akt signaling pathway 2/49359/87530.60305880.65758240.5335647FN1/CCND1 2
hsa04022Environmental Information ProcessingSignal transduction hsa04022cGMP-PKG signaling pathway 1/49166/87530.60968970.65758240.5335647PPIF 1
hsa04630Environmental Information ProcessingSignal transduction hsa04630JAK-STAT signaling pathway 1/49166/87530.60968970.65758240.5335647CCND1 1
hsa03008Genetic Information Processing Translation hsa03008Ribosome biogenesis in eukaryotes 1/49167/87530.61191700.65758240.5335647TCOF1 1
hsa04141Genetic Information Processing Folding, sorting and degradationhsa04141Protein processing in endoplasmic reticulum 1/49170/87530.61852420.65975910.5353309CALR 1
hsa05152Human Diseases Infectious disease: bacterial hsa05152Tuberculosis 1/49180/87530.63976230.67739540.5496410LAMP1 1
hsa04613Organismal Systems Immune system hsa04613Neutrophil extracellular trap formation 1/49191/87530.66178730.69458880.5635918PPIF 1
hsa05202Human Diseases Cancer: overview hsa05202Transcriptional misregulation in cancer 1/49193/87530.66564760.69458880.5635918IGFBP3 1
hsa05415Human Diseases Cardiovascular disease hsa05415Diabetic cardiomyopathy 1/49203/87530.68431090.70892640.5752254PPIF 1
hsa04015Environmental Information ProcessingSignal transduction hsa04015Rap1 signaling pathway 1/49210/87530.69676400.71667160.5815098CTNNB1 1
hsa04810Cellular Processes Cell motility hsa04810Regulation of actin cytoskeleton 1/49229/87530.72818990.73844610.5991777FN1 1
hsa04820NA NA hsa04820Cytoskeleton in muscle cells 1/49229/87530.72818990.73844610.5991777FN1 1
hsa04020Environmental Information ProcessingSignal transduction hsa04020Calcium signaling pathway 1/49253/87530.76335650.76869470.6237215PPIF 1
hsa05206Human Diseases Cancer: overview hsa05206MicroRNAs in cancer 1/49310/87530.82997900.82997900.6734478CCND1 1
In [239]:
pway
#
# over-representation test
#
#...@organism 	 hsa 
#...@ontology 	 KEGG 
#...@keytype 	 ENTREZID 
#...@gene 	 chr [1:59] "2335" "595" "3856" "2947" "928" "9741" "4192" "5052" "5230" ...
#...pvalues adjusted by 'BH' with cutoff <0.1 
#...11 enriched terms found
'data.frame':	11 obs. of  11 variables:
 $ category   : chr  "Cellular Processes" "Cellular Processes" "Metabolism" "Human Diseases" ...
 $ subcategory: chr  "Cell growth and death" "Cell growth and death" "Carbohydrate metabolism" "Neurodegenerative disease" ...
 $ ID         : chr  "hsa04218" "hsa04110" "hsa00010" "hsa05012" ...
 $ Description: chr  "Cellular senescence" "Cell cycle" "Glycolysis / Gluconeogenesis" "Parkinson disease" ...
 $ GeneRatio  : chr  "7/49" "7/49" "4/49" "7/49" ...
 $ BgRatio    : chr  "156/8753" "157/8753" "67/8753" "266/8753" ...
 $ pvalue     : num  2.29e-05 2.38e-05 5.13e-04 6.37e-04 7.87e-04 ...
 $ p.adjust   : num  0.00172 0.00172 0.02266 0.02266 0.02266 ...
 $ qvalue     : num  0.00139 0.00139 0.01839 0.01839 0.01839 ...
 $ geneID     : chr  "CCND1/SQSTM1/CDK1/CCNB1/FOXM1/IGFBP3/MYBL2" "CCND1/TFDP1/CDK1/CCNB1/PCNA/MCM3/NDC80" "PGK1/PKM/LDHB/ALDH3A1" "UBB/TUBA1A/PPIF/PSMB6/TUBA4A/TUBB4B/UBC" ...
 $ Count      : int  7 7 4 7 4 9 4 5 5 6 ...
#...Citation
 T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu.
 clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.
 The Innovation. 2021, 2(3):100141 
In [76]:
pathway_hallmark = openxlsx::read.xlsx(file.path('./resources/pathway', 'Gene_signature_list_paper_supp.xlsx'))
In [77]:
pathway_hallmark
A data.frame: 200 × 72
HALLMARK_TNFA_SIGNALING_VIA_NFKBHALLMARK_HYPOXIAHALLMARK_CHOLESTEROL_HOMEOSTASISHALLMARK_TGF_BETA_SIGNALINGHALLMARK_DNA_REPAIRHALLMARK_G2M_CHECKPOINTHALLMARK_APOPTOSISHALLMARK_NOTCH_SIGNALINGHALLMARK_ADIPOGENESISHALLMARK_ESTROGEN_RESPONSE_EARLY⋯HALLMARK_IL2_STAT5_SIGNALINGHALLMARK_ALLOGRAFT_REJECTIONHALLMARK_KRAS_SIGNALING_UPFATTY_ACID_BETA_OXIDATIONS.GENES.ITAYG2M.GENES.ITAYCELL_CYCLENRF2_ROMOROKEGG_GLUTATHIONE_METABOLISMGO_ANTIOXIDANT_ACTIVITY
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>⋯<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
1JUNB PGK1 FDPS TGFBR1 POLR2H AURKA CASP3 JAG1 FABP4 GREB1 ⋯SOCS2 PTPRC ANGPTL4ACADM MCM5 HMGB2 MCM5 TXNRD1 ANPEPALB
2CXCL2 PDK1 CYP51A1SMAD7 POLR2A CCNA2 CASP9 NOTCH3ADIPOQ CA12 ⋯CISH IL12B ITGA2 ACADS PCNA CDK1 PCNA TKT G6PD ALOX5AP
3ATF3 GBE1 IDI1 TGFB1 POLR2G TOP2A DFFA NOTCH2PPARG SLC9A3R1⋯PIM1 TGFB1 SPRY2 ACADVLTYMS NUSAP1TYMS SRXN1 GCLC APOA4
4NFKBIA PFKL FDFT1 SMURF2 POLR2E CCNB2 CASP7 APH1A LIPE MYB ⋯IL2RA IL12A HBEGF BDH2 FEN1 UBE2C FEN1 NQO1 GCLM APOE
5TNFAIP3 ALDOA DHCR7 SMURF1 POLR2J CENPA CFLAR HES1 DGAT1 ANXA9 ⋯TNFRSF4 CD3E RBP4 CPT1A MCM2 BIRC5 MCM2 GCLC GGCT APOM
6PTGS2 ENO2 SQLE BMPR2 POLR2F BIRC5 BIRC3 CCND1 LPL IGFBP4 ⋯SOCS1 CD3D HSD11B1CPT1B MCM4 TPX2 MCM4 G6PD GGT1 CAT
7CXCL1 PGM1 HMGCS1 SKIL POLR2C CDC20 PMAIP1 FZD1 CPT2 SYBU ⋯TNFRSF9 CD28 ETV4 ECH1 RRM1 TOP2A RRM1 FTH1 GGT5 CCS
8IER3 NDRG1 NSDHL SKI POLR2K PLK1 CASP8 PSEN2 CD36 NPY1R ⋯XBP1 LYN GLRX ECHS1 UNG NDC80 UNG EPHX1 GGT6 CLIC2
9CD83 HK2 LSS ACVR1 GTF2H3 TTK JUN FZD7 GPAM PDZK1 ⋯RRAGD HCLS1 DUSP6 HADHB GINS2 CKS2 GINS2 DDC GGT7 CYGB
10CCL20 ALDOC MVD PMEPA1 POLR2D PRC1 BCL2L11DTX1 ADIPOR2 NRIP1 ⋯HK2 IL18 SCG5 PPARA MCM6 NUF2 MCM6 CBR3 GPX1 DUOX1
11CXCL3 GPI LDLR NCOR2 ERCC3 NDC80 MCL1 DLL1 ACAA2 MLPH ⋯PHLDA1 CRTAM ETV5 PPARD CDCA7 CKS1B CDCA7 UIP1 GPX2 DUOX2
12MAFF MXI1 TM7SF2 SERPINE1DDB2 KIF11 IL1B FZD5 ETFB HSPB8 ⋯IL2RB IFNG ITGB2 CPT1C DTL MKI67 DTL UCHL1 GPX3 EPX
13NFKB2 SLC2A1 ALDOC JUNB POLR1C NUSAP1 SPTAN1 MAML2 ACOX1 EGR3 ⋯CTLA4 CD3G AKT2 NA PRIM1 TMPO PRIM1 TXN GPX4 FABP1
14TNFAIP2 P4HA1 EBP SMAD1 XPC CKS2 DIABLO NOTCH1ACADM KRT19 ⋯NFIL3 CD86 PPBP NA UHRF1 CENPF UHRF1 TSPAN7 GPX5 GPX1
15HBEGF ADM SCD SMAD6 PCNA KIF2C BAX PSENENHADH LRIG1 ⋯CD83 IL10 G0S2 NA MLF1IP TACC3 MLF1IP TRIM16 GPX6 GPX2
16KLF6 P4HA2 PMVK PPP1R15APOLR2I MKI67 BIK WNT5A IDH1 KDM4B ⋯IKZF2 UBE2N GABRA3 NA HELLS FAM64AHELLS TM4SF20 GPX7 GPX3
17BIRC3 ENO1 MVK TGIF1 SUPT4H1AURKB IL1A CUL1 SORBS1 PGR ⋯IL10 BCL10 IRF8 NA RFC2 SMC4 RFC2 TGFB2 GSR GPX4
18PLAUR PFKP LPL FURIN POLD3 TPX2 BID WNT2 ACADS RHOBTB3 ⋯TNFRSF18CD4 BIRC3 NA RPA2 CCNB2 RPA2 TFRC GSS GPX5
19ZFP36 AK4 SC5D SMAD3 POLR3GLSMC4 CDKN1A DTX4 UCK1 TPD52L1 ⋯DHRS3 LCK FGF9 NA NASP CKAP2LNASP TALDO1 GSTA1GPX6
20ICAM1 FAM162AFADS2 FKBP1A POLR3C BUB1 GADD45ASAP30 SCP2 ELOVL2 ⋯ECM1 NCK1 DCBLD2 NA RAD51AP1CKAP2 RAD51AP1SQSTM1 GSTA2GPX7
21JUN PFKFB3 HMGCR MAP3K7 GTF2B CENPF DDIT3 PPARD DECR1 RET ⋯ADAM19 C2 INHBA NA GMNN AURKB GMNN SPP1 GSTA3GPX8
22EGR3 VEGFA HSD17B7BMPR1A POLR1D RACGAP1 CDKN1B KAT2A CDKN2C TPBG ⋯SLC2A3 HLA-A TFPI NA WDR76 BUB1 WDR76 SPANXB1 GSTA4GSR
23IL1B BNIP3L ANXA13 CTNNB1 NCBP2 CENPE TNF HEYL TALDO1 TFF1 ⋯HIPK2 ITGB2 TSPAN1 NA SLBP KIF11 SLBP SPANXA1 GSTA5GSTA1
24BCL2A1 TPI1 SREBF2 HIPK2 NELFE AC027237.1GSN SKP1 TST MAPT ⋯BATF3 HLA-DQA1ADAM8 NA CCNE2 ANP32ECCNE2 SORD GSTK1GSTK1
25PPP1R15AERO1A PCYT2 KLF10 GTF2F1 UBE2C TNFSF10RBX1 MCCC1 SCNN1A ⋯BHLHE40 CD1D SLPI NA UBR7 TUBB4BUBR7 SLC7A11 GSTM1GSTM2
26ZC3H12A KDM3A ACSS2 BMP2 ERCC5 MCM6 CASP6 TCF7L2PGM1 ABAT ⋯PTGER2 CD80 PRKG2 NA POLD3 GTSE1 POLD3 SLC6A6 GSTM2GSTO1
27SOD2 CCNG2 ATF3 ENG LIG1 MCM3 SQSTM1 ARRB1 REEP5 FLNB ⋯DENND5A HLA-DRA MMP11 NA MSH2 KIF20BMSH2 SLC38A6 GSTM3GSTO2
28NR4A2 LDHA ADH4 APC ERCC1 PTTG1 FASLG LFNG BCL2L13 XBP1 ⋯ITIH5 THY1 MMP10 NA ATAD2 HJURP ATAD2 SFN GSTM4GSTP1
29IL1A GYS1 ETHE1 PPM1A ERCC4 CDK1 EGR3 PRKCA SLC25A10CELSR2 ⋯PHTF2 TLR1 TMEM158NA RAD51 CDCA3 RAD51 SERPINE1GSTM5GSTT1
30RELB GAPDH ECH1 XIAP POLD4 KIF4A CD44 DTX2 ME1 RAB31 ⋯GADD45B HLA-G TNFAIP3NA RRM2 HN1 RRM2 RRM2 GSTO1GSTZ1
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋱⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
171IL12B MYH9 NANANAHMGN2 NANADHRS7B INHBB ⋯UMPS RIPK2 FUCA1 NANANANANANANA
172IL6ST CDKN1C NANANASRSF10 NANAMIGA2 BHLHE40⋯HUWE1 IKBKB PLVAP NANANANANANANA
173SLC16A6 GRHPR NANANASNRPD1 NANAMGLL CALB2 ⋯COL6A1 GCNT1 ADAM17 NANANANANANANA
174ABCA1 PCK1 NANANACASP8AP2NANAITSN1 FASN ⋯ABCB1 SOCS5 AVL9 NANANANANANANA
175HES1 INHA NANANASMARCC1 NANADHCR7 CHPT1 ⋯RNH1 IRF8 ADAMDEC1NANANANANANANA
176BCL6 HSPA5 NANANASLC12A2 NANARREB1 MYBBP1A⋯IRF8 TAP2 HKDC1 NANANANANANANA
177IRS2 NDST2 NANANANOTCH2 NANACMBL ELOVL5 ⋯GUCY1B1 EIF4G3 MAP7 NANANANANANANA
178SLC2A3 NEDD4L NANANATNPO2 NANAUBC DYNLT3 ⋯AHCY ABI1 IL7R NANANANANANANA
179CEBPD TPBG NANANASMAD3 NANAATP5PO ABLIM1 ⋯PRAF2 CCL7 RBM4 NANANANANANANA
180IL23A XPNPEP1 NANANAMAP3K20 NANAPRDX3 SOX3 ⋯GSTO1 IL2RB BPGM NANANANANANANA
181SMAD3 IL6 NANANAHSPA8 NANADBT SLC24A3⋯TWSG1 BRCA1 ENG NANANANANANANA
182TAP1 SLC6A6 NANANAG3BP1 NANANDUFS3 RAB17 ⋯CDC42SE2FGR GFPT2 NANANANANANANA
183MSC MAP3K1 NANANAPTTG3P NANANKIRAS1MAST4 ⋯PLAGL1 IL18RAPPLAU NANANANANANANA
184IFIH1 LDHC NANANADMD NANARAB34 KCNK5 ⋯APLP1 MRPL3 GNG11 NANANANANANANA
185IL15RA AKAP12 NANANAMEIS1 NANACIDEA ELF1 ⋯AKAP2 CXCL13 PTCD2 NANANANANANANA
186TNIP2 TES NANANAHNRNPU NANAUQCRQ RPS6KA2⋯PLPP1 CAPG MAP3K1 NANANANANANANA
187BCL3 KIF5A NANANASRSF2 NANAPEX14 ISG20L2⋯SPRY4 EIF5A CBL NANANANANANANA
188PANX1 LALBA NANANAMT2A NANABCL6 IL6ST ⋯SCN9A RPS3A CXCR4 NANANANANANANA
189FJX1 COL5A1 NANANANUP98 NANACOX6A1 SYNGR1 ⋯SHE GALNT1 NIN NANANANANANANA
190EDN1 GPC1 NANANAEWSR1 NANADNAJB9 SH3BP5 ⋯PDCD2L ST8SIA4IKZF1 NANANANANANANA
191EIF1 HDLBP NANANAKIF5B NANAMAP4K3 ALDH3B1⋯CCND3 CCL13 WDR33 NANANANANANANA
192BMP2 ILVBL NANANAMTF2 NANAANGPT1 THSD4 ⋯LRIG1 RPL3L MYCN NANANANANANANA
193DUSP4 NCAN NANANAE2F4 NANAUBQLN1 CLIC3 ⋯SWAP70 LY75 FCER1G NANANANANANANA
194PDLIM5 TGM2 NANANABCL3 NANANDUFB7 NXT1 ⋯SLC39A8 TAPBP PECAM1 NANANANANANANA
195ICOSLG ETS1 NANANAPURA NANASLC19A1NAV2 ⋯RABGAP1LNOS2 CCSER2 NANANANANANANA
196GFPT2 HOXB9 NANANAMEIS2 NANAABCB8 RRP12 ⋯TGM2 RPL9 SNAP91 NANANANANANANA
197KLF2 SELENBP1NANANAPAFAH1B1NANAPQLC3 ADCY1 ⋯PNP BCAT1 EVI5 NANANANANANANA
198TNC FOSL2 NANANAWRN NANAPOR DHCR7 ⋯AGER IL9 TNFRSF1BNANANANANANANA
199SERPINB8SULT2B1 NANANAH2AFV NANAUCP2 MICB ⋯ETV4 IL27RA GPNMB NANANANANANANA
200MXD1 TGFB3 NANANAODF2 NANAUQCR11 AKAP1 ⋯CD86 DYRK3 TPH1 NANANANANANANA
In [125]:
drivers = as.data.frame(drivers)
for (pw in colnames(pathway_hallmark)){
    drivers[pw] = drivers$V1 %in% pathway_hallmark[,pw]
}
In [128]:
drivers
A data.frame: 63 × 54
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank⋯HALLMARK_IL2_STAT5_SIGNALINGHALLMARK_ALLOGRAFT_REJECTIONHALLMARK_KRAS_SIGNALING_UPFATTY_ACID_BETA_OXIDATIONS.GENES.ITAYG2M.GENES.ITAYCELL_CYCLENRF2_ROMOROKEGG_GLUTATHIONE_METABOLISMGO_ANTIOXIDANT_ACTIVITY
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><int>⋯<lgl><lgl><lgl><lgl><lgl><lgl><lgl><lgl><lgl><lgl>
FN1 0.0079933780.0007537607 1.907695e-0410FALSEFALSEFALSEFALSE 1⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
HIST1H2BD0.0038648270.0005008594 1.813786e-0410FALSEFALSEFALSEFALSE 2⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
CCND1 0.0023067440.0002728280 1.020445e-0410FALSEFALSEFALSEFALSE 3⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
KRT8 0.0041713730.0006077977 7.349687e-0510FALSEFALSEFALSEFALSE 4⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
GSTM3 0.0065702270.0004776801 1.470234e-0410FALSEFALSEFALSEFALSE 5⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE TRUEFALSE
CD9 0.0042869820.0004431204 1.179431e-0410FALSEFALSEFALSEFALSE 6⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
H2AFZ 0.0014095510.0002377385 4.583457e-0510FALSE TRUEFALSE TRUE 7⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
LAPTM4A 0.0055573910.0006989103 1.191966e-0410FALSEFALSEFALSEFALSE 8⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
MDK 0.0044854940.0007073663 9.334373e-0510FALSEFALSEFALSEFALSE 9⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
PRDX1 0.0057532930.0005948305 1.307083e-0410FALSEFALSE TRUE TRUE10⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSE TRUEFALSE TRUE
PGK1 0.0029370070.0004105406 8.659618e-0510FALSEFALSEFALSEFALSE11⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
NQO1 0.0071891720.0005834166 1.745988e-0410FALSEFALSE TRUE TRUE12⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSE TRUEFALSE TRUE
ALPP 0.0037903640.0003220504 1.560096e-0410FALSEFALSEFALSEFALSE13⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
MT2A 0.0029928150.0007070334-5.982608e-0510FALSEFALSEFALSEFALSE14⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
PKM 0.0024184760.0004418046 8.013423e-0510FALSEFALSEFALSEFALSE15⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
UBB 0.0020930290.0004443382-5.899295e-0510FALSEFALSEFALSEFALSE16⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
FOSL1 0.0042657690.0005058976-9.429040e-0510 TRUEFALSEFALSEFALSE17⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
CTSA 0.0040279710.0005851937 1.295741e-0410FALSEFALSEFALSEFALSE18⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
UBE2S 0.0047840570.0005292148-1.516910e-0410FALSEFALSEFALSEFALSE19⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
HSPB1 0.0020172960.0003566292 7.373099e-0510FALSEFALSEFALSEFALSE20⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
MYL6 0.0085084580.0008462955 1.682113e-0410FALSEFALSEFALSEFALSE21⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
TCOF1 0.0022865290.0003146553-6.928714e-0510FALSEFALSEFALSEFALSE22⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
TFDP1 0.0033304280.0003013794-9.927206e-0510 TRUEFALSEFALSEFALSE23⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
CSTB 0.0031482890.0003750370-1.198390e-0410FALSEFALSEFALSEFALSE24⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
JUND 0.0016843240.0003056773 1.057126e-0410 TRUEFALSEFALSEFALSE25⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
TGM2 0.0053295820.0004317851-1.178054e-0410FALSEFALSEFALSEFALSE26⋯ TRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
SQSTM1 0.0028696040.0004758771 9.440278e-0510FALSEFALSEFALSEFALSE27⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSE TRUEFALSEFALSE
HIST1H4C 0.0037515880.0004830967-1.076414e-0410FALSEFALSEFALSEFALSE28⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
CKS1B 0.0026070020.0003412548 9.403953e-05 9FALSEFALSEFALSEFALSE29⋯FALSEFALSEFALSEFALSEFALSE TRUE TRUEFALSEFALSEFALSE
CD24 0.0027278510.0002715580 1.166907e-05 9FALSEFALSEFALSEFALSE30⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋱⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
CENPN 0.00212675123.911722e-04 8.123492e-058FALSEFALSEFALSEFALSE34⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
HMGA1 0.00125680325.449500e-04-4.842230e-057 TRUEFALSEFALSEFALSE35⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
CD81 0.00268137972.755875e-04 1.560641e-047FALSEFALSEFALSEFALSE36⋯ TRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
PPIF 0.00139789991.616978e-04-1.086447e-056FALSEFALSEFALSEFALSE37⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
PSMB6 0.00158162401.423927e-04 9.055104e-055FALSEFALSEFALSEFALSE38⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
CDK1 0.00078929861.556827e-04-6.600696e-055FALSEFALSEFALSEFALSE39⋯FALSEFALSEFALSEFALSEFALSE TRUE TRUEFALSEFALSEFALSE
CCNB1 0.00096002527.355914e-05 3.018131e-075FALSEFALSEFALSEFALSE40⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
SERPINH10.00113318892.452786e-04 5.132065e-055FALSEFALSEFALSEFALSE41⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
FOXM1 0.00066866761.551108e-04-7.297643e-065 TRUEFALSEFALSEFALSE42⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
LAMP1 0.00146759473.839450e-04 7.594953e-055FALSEFALSEFALSEFALSE43⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
PPP1R14B0.00119375281.994650e-04-8.283027e-064FALSEFALSEFALSEFALSE44⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
TFPI2 0.00116053802.944522e-04 7.088958e-054FALSEFALSEFALSEFALSE45⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
ALDH3A1 0.00130553771.966169e-04 4.354592e-074FALSE TRUEFALSE TRUE46⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSE TRUEFALSEFALSE
CALD1 0.00067213599.202680e-06 3.342999e-073FALSEFALSEFALSEFALSE47⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
TUBA4A 0.00058295211.696022e-04 1.596368e-073FALSEFALSEFALSEFALSE48⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
IGFBP3 0.00102586542.486552e-04 1.015227e-053FALSEFALSEFALSEFALSE49⋯FALSEFALSE TRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
TUBB4B 0.00038309044.519722e-05 7.412989e-082FALSEFALSEFALSEFALSE50⋯FALSEFALSEFALSEFALSEFALSE TRUE TRUEFALSEFALSEFALSE
MYBL2 0.00024020411.968738e-04 6.863686e-062 TRUEFALSEFALSEFALSE51⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
PHGDH 0.00041736705.805091e-05 2.442747e-072FALSEFALSEFALSEFALSE52⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
UBC 0.00021020676.868151e-05 1.274175e-072FALSEFALSEFALSEFALSE53⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
PCNA 0.00044944111.240919e-04 5.007451e-052FALSEFALSEFALSEFALSE54⋯FALSEFALSEFALSEFALSE TRUEFALSE TRUEFALSEFALSEFALSE
HIST1H1C0.00048376571.441257e-04 9.379254e-062FALSEFALSEFALSEFALSE55⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
CTNNB1 0.00013813280.000000e+00-9.900118e-091FALSEFALSEFALSEFALSE56⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
CALR 0.00024549710.000000e+00-1.203399e-071FALSEFALSEFALSEFALSE57⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
MCM3 0.00017890020.000000e+00 2.958242e-101FALSEFALSEFALSEFALSE58⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
CENPF 0.00023769770.000000e+00 6.111640e-091FALSEFALSEFALSEFALSE59⋯FALSEFALSEFALSEFALSEFALSE TRUE TRUEFALSEFALSEFALSE
ANXA2 0.00018179790.000000e+00-2.258579e-101FALSEFALSEFALSEFALSE60⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
TOP2A 0.00026935510.000000e+00-8.096725e-101FALSEFALSEFALSEFALSE61⋯FALSEFALSEFALSEFALSEFALSE TRUE TRUEFALSEFALSEFALSE
NDC80 0.00022566190.000000e+00-6.941362e-081FALSEFALSEFALSEFALSE62⋯FALSEFALSEFALSEFALSEFALSE TRUE TRUEFALSEFALSEFALSE
SMC4 0.00019395610.000000e+00-6.988958e-081FALSEFALSEFALSEFALSE63⋯FALSEFALSEFALSEFALSEFALSE TRUE TRUEFALSEFALSEFALSE
In [131]:
drivers[drivers$HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY,]
A data.frame: 2 × 54
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank⋯HALLMARK_IL2_STAT5_SIGNALINGHALLMARK_ALLOGRAFT_REJECTIONHALLMARK_KRAS_SIGNALING_UPFATTY_ACID_BETA_OXIDATIONS.GENES.ITAYG2M.GENES.ITAYCELL_CYCLENRF2_ROMOROKEGG_GLUTATHIONE_METABOLISMGO_ANTIOXIDANT_ACTIVITY
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><int>⋯<lgl><lgl><lgl><lgl><lgl><lgl><lgl><lgl><lgl><lgl>
10PRDX10.0057532930.00059483050.000130708310FALSEFALSETRUETRUE10⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUE
12NQO1 0.0071891720.00058341660.000174598810FALSEFALSETRUETRUE12⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUE
In [156]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [157]:
drivers[drivers$is_in_ROS,]
A data.table: 2 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
PRDX10.0057532930.00059483050.000130708310FALSEFALSETRUETRUE59
NQO1 0.0071891720.00058341660.000174598810FALSEFALSETRUETRUE33
In [146]:
drivers[drivers$is_in_Pathway,]
A data.frame: 4 × 55
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank⋯HALLMARK_ALLOGRAFT_REJECTIONHALLMARK_KRAS_SIGNALING_UPFATTY_ACID_BETA_OXIDATIONS.GENES.ITAYG2M.GENES.ITAYCELL_CYCLENRF2_ROMOROKEGG_GLUTATHIONE_METABOLISMGO_ANTIOXIDANT_ACTIVITYrank_shap_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><int>⋯<lgl><lgl><lgl><lgl><lgl><lgl><lgl><lgl><lgl><dbl>
7H2AFZ 0.0014095510.00023773854.583457e-0510FALSE TRUEFALSETRUE 7⋯FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE37
10PRDX1 0.0057532930.00059483051.307083e-0410FALSEFALSE TRUETRUE10⋯FALSEFALSEFALSEFALSEFALSEFALSE TRUEFALSE TRUE 5
12NQO1 0.0071891720.00058341661.745988e-0410FALSEFALSE TRUETRUE12⋯FALSEFALSEFALSEFALSEFALSEFALSE TRUEFALSE TRUE 3
46ALDH3A10.0013055380.00019661694.354592e-07 4FALSE TRUEFALSETRUE46⋯FALSEFALSEFALSEFALSEFALSEFALSE TRUEFALSEFALSE39
In [ ]:

In [174]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t7_t14_cyc'
read_dir <- file.path(read_dir,run_name)
In [175]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
BIN1 0.0033546050.0003882261 0.000515350110FALSEFALSEFALSEFALSE
CKS1B0.0027099940.0005753519 0.000454056910FALSEFALSEFALSEFALSE
PRDX10.0031960590.0005124655 0.000408712810FALSEFALSE TRUE TRUE
KRT8 0.0038899180.0002229171 0.000548130810FALSEFALSEFALSEFALSE
SRSF30.0028382130.0003422896 0.000434381110FALSEFALSEFALSEFALSE
TPM1 0.0023733090.0003037851-0.000529684510FALSEFALSEFALSEFALSE
In [176]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [177]:
drivers[drivers$is_in_ROS,]
A data.table: 2 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
PRDX10.0031960590.00051246550.000408712810FALSEFALSETRUETRUE1322
NQO1 0.0020750330.00021670710.0003687808 9FALSEFALSETRUETRUE2324
In [178]:
drivers[drivers$is_in_Pathway,]
A data.table: 4 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
PRDX1 0.0031960590.00051246550.000408712810FALSEFALSE TRUETRUE1322
H2AFZ 0.0040756580.00058476430.000647578710FALSE TRUEFALSETRUE 8 8
ALDH3A10.0044520390.00054620900.000762479310FALSE TRUEFALSETRUE 6 5
NQO1 0.0020750330.00021670710.0003687808 9FALSEFALSE TRUETRUE2324
In [164]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t14_ncyc_cyc'
read_dir <- file.path(read_dir,run_name)
In [165]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
HLA-B 0.0066118370.0014072035-0.00388835810FALSEFALSEFALSEFALSE
H2AFZ 0.0015434790.0003817574 0.00159619110FALSE TRUEFALSE TRUE
UBC 0.0030148710.0006034041-0.00355204510FALSEFALSEFALSEFALSE
IGFBP50.0117281110.0037999116 0.01300407910FALSEFALSEFALSEFALSE
GSTM3 0.0092013450.0016104131 0.00469491910FALSEFALSEFALSEFALSE
SAT1 0.0038243560.0007941552-0.00198726110FALSEFALSEFALSEFALSE
In [166]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [167]:
drivers[drivers$is_in_ROS,]
A data.table: 3 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
JUNB0.00168353710.0008096141-0.00298141387 TRUEFALSETRUETRUE20 9
SOD20.00022155760.0000000000 0.00075896761FALSEFALSETRUETRUE6339
PFKP0.00024212560.0000000000 0.00020917141FALSEFALSETRUETRUE6163
In [168]:
drivers[drivers$is_in_Pathway,]
A data.table: 5 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
H2AFZ 0.00154347910.0003817574 0.001596190810FALSE TRUEFALSETRUE2220
JUNB 0.00168353710.0008096141-0.0029814138 7 TRUEFALSE TRUETRUE20 9
SOD2 0.00022155760.0000000000 0.0007589676 1FALSEFALSE TRUETRUE6339
LGALS10.00013876970.0000000000 0.0001830989 1FALSE TRUEFALSETRUE7265
PFKP 0.00024212560.0000000000 0.0002091714 1FALSEFALSE TRUETRUE6163

t0_14¶

In [163]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
read_dir <- file.path(read_dir,run_name)
In [164]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
FN1 0.0079933780.00075376071.907695e-0410FALSEFALSEFALSEFALSE
HIST1H2BD0.0038648270.00050085941.813786e-0410FALSEFALSEFALSEFALSE
CCND1 0.0023067440.00027282801.020445e-0410FALSEFALSEFALSEFALSE
KRT8 0.0041713730.00060779777.349687e-0510FALSEFALSEFALSEFALSE
GSTM3 0.0065702270.00047768011.470234e-0410FALSEFALSEFALSEFALSE
CD9 0.0042869820.00044312041.179431e-0410FALSEFALSEFALSEFALSE
In [767]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [768]:
drivers[drivers$is_in_Pathway,]
A data.table: 4 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
H2AFZ 0.0014095510.00023773854.583457e-0510FALSE TRUEFALSETRUE3741
PRDX1 0.0057532930.00059483051.307083e-0410FALSEFALSE TRUETRUE 5 9
NQO1 0.0071891720.00058341661.745988e-0410FALSEFALSE TRUETRUE 3 3
ALDH3A10.0013055380.00019661694.354592e-07 4FALSE TRUEFALSETRUE3949
In [283]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [286]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
    file_name = paste0('driver_info_',i,'.csv')
    tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
    df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
    
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [287]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [288]:
plot_df
A data.frame: 1020 × 3
Genevariablevalue
<chr><chr><dbl>
ACTG2 run00.0020976764
ALDH3A1run00.0030892894
ALPP run00.0034531571
ANXA2 run0 NA
APP run0 NA
ASF1B run0 NA
ATF3 run00.0003612373
AURKA run00.0015353375
AURKB run0 NA
CALD1 run0 NA
CALR run0 NA
CCNA2 run0 NA
CCNB1 run0 NA
CCND1 run00.0026891035
CD24 run00.0028103908
CD81 run00.0037515941
CD9 run00.0041851407
CDC20 run00.0008733733
CDK1 run00.0011039842
CEBPB run0 NA
CEBPD run0 NA
CENPA run0 NA
CENPF run0 NA
CENPM run0 NA
CENPN run00.0030142995
CENPV run0 NA
CKS1B run0 NA
CSTB run00.0027676788
CTNNB1 run0 NA
CTSA run00.0037647824
⋮⋮⋮
PGK1 run90.002112776
PHGDH run90.001609636
PKM run90.002349730
PLK1 run9 NA
PPIF run90.001792963
PPP1R14Brun90.002269739
PRDX1 run90.005420250
PSAP run9 NA
PSMB6 run90.002708103
RAN run9 NA
RANBP1 run90.003856269
RFC4 run9 NA
SERPINH1run90.001784495
SMC4 run9 NA
SQSTM1 run90.003209935
STAT1 run9 NA
TCOF1 run90.002694048
TFDP1 run90.003229773
TFPI2 run9 NA
TGM2 run90.005533601
TOP2A run90.002693551
TUBA1A run90.002486760
TUBA1B run90.000902929
TUBA4A run9 NA
TUBB4B run90.001672432
TYMS run9 NA
UBB run90.001767165
UBC run9 NA
UBE2S run90.004712779
UCHL1 run9 NA
In [289]:
pathway_hallmark = openxlsx::read.xlsx(file.path('./resources/pathway', 'Gene_signature_list_paper_supp.xlsx'))
In [324]:
plot_df$pw = plot_df$Gene %in% pathway_hallmark$HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY
In [291]:
pathway_hallmark$HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY
  1. 'ABCC1'
  2. 'ATOX1'
  3. 'CAT'
  4. 'CDKN2D'
  5. 'EGLN2'
  6. 'ERCC2'
  7. 'FES'
  8. 'FTL'
  9. 'G6PD'
  10. 'GCLC'
  11. 'GCLM'
  12. 'GLRX'
  13. 'GLRX2'
  14. 'GPX3'
  15. 'GPX4'
  16. 'GSR'
  17. 'HHEX'
  18. 'HMOX2'
  19. 'IPCEF1'
  20. 'JUNB'
  21. 'LAMTOR5'
  22. 'LSP1'
  23. 'MBP'
  24. 'MGST1'
  25. 'MPO'
  26. 'MSRA'
  27. 'NDUFA6'
  28. 'NDUFB4'
  29. 'NDUFS2'
  30. 'NQO1'
  31. 'OXSR1'
  32. 'PDLIM1'
  33. 'PFKP'
  34. 'PRDX1'
  35. 'PRDX2'
  36. 'PRDX4'
  37. 'PRDX6'
  38. 'PRNP'
  39. 'PTPA'
  40. 'SBNO2'
  41. 'SCAF4'
  42. 'SELENOS'
  43. 'SOD1'
  44. 'SOD2'
  45. 'SRXN1'
  46. 'STK25'
  47. 'TXN'
  48. 'TXNRD1'
  49. 'TXNRD2'
  50. NA
  51. NA
  52. NA
  53. NA
  54. NA
  55. NA
  56. NA
  57. NA
  58. NA
  59. NA
  60. NA
  61. NA
  62. NA
  63. NA
  64. NA
  65. NA
  66. NA
  67. NA
  68. NA
  69. NA
  70. NA
  71. NA
  72. NA
  73. NA
  74. NA
  75. NA
  76. NA
  77. NA
  78. NA
  79. NA
  80. NA
  81. NA
  82. NA
  83. NA
  84. NA
  85. NA
  86. NA
  87. NA
  88. NA
  89. NA
  90. NA
  91. NA
  92. NA
  93. NA
  94. NA
  95. NA
  96. NA
  97. NA
  98. NA
  99. NA
  100. NA
  101. NA
  102. NA
  103. NA
  104. NA
  105. NA
  106. NA
  107. NA
  108. NA
  109. NA
  110. NA
  111. NA
  112. NA
  113. NA
  114. NA
  115. NA
  116. NA
  117. NA
  118. NA
  119. NA
  120. NA
  121. NA
  122. NA
  123. NA
  124. NA
  125. NA
  126. NA
  127. NA
  128. NA
  129. NA
  130. NA
  131. NA
  132. NA
  133. NA
  134. NA
  135. NA
  136. NA
  137. NA
  138. NA
  139. NA
  140. NA
  141. NA
  142. NA
  143. NA
  144. NA
  145. NA
  146. NA
  147. NA
  148. NA
  149. NA
  150. NA
  151. NA
  152. NA
  153. NA
  154. NA
  155. NA
  156. NA
  157. NA
  158. NA
  159. NA
  160. NA
  161. NA
  162. NA
  163. NA
  164. NA
  165. NA
  166. NA
  167. NA
  168. NA
  169. NA
  170. NA
  171. NA
  172. NA
  173. NA
  174. NA
  175. NA
  176. NA
  177. NA
  178. NA
  179. NA
  180. NA
  181. NA
  182. NA
  183. NA
  184. NA
  185. NA
  186. NA
  187. NA
  188. NA
  189. NA
  190. NA
  191. NA
  192. NA
  193. NA
  194. NA
  195. NA
  196. NA
  197. NA
  198. NA
  199. NA
  200. NA
In [292]:
pathway_hallmark$HALLMARK_FATTY_ACID_METABOLISM
  1. 'ACAA1'
  2. 'ACAA2'
  3. 'ACADL'
  4. 'ACADM'
  5. 'ACOT8'
  6. 'ACOX1'
  7. 'ACSL1'
  8. 'ALDH3A2'
  9. 'CCDC58'
  10. 'CPT2'
  11. 'CYP4A11'
  12. 'DECR1'
  13. 'ECH1'
  14. 'ECI1'
  15. 'ELOVL5'
  16. 'FABP1'
  17. 'FABP2'
  18. 'HADH'
  19. 'HIBCH'
  20. 'HMGCL'
  21. 'HSD17B11'
  22. 'IDH1'
  23. 'ME1'
  24. 'MGLL'
  25. 'MLYCD'
  26. 'PCBD1'
  27. 'RETSAT'
  28. 'S100A10'
  29. 'SUCLG1'
  30. 'VNN1'
  31. 'EHHADH'
  32. 'ALDH9A1'
  33. 'HADHB'
  34. 'ECHS1'
  35. 'ACADS'
  36. 'CA2'
  37. 'HSD17B10'
  38. 'ALDH1A1'
  39. 'ACADVL'
  40. 'HSD17B4'
  41. 'CA4'
  42. 'ADH1C'
  43. 'ADH7'
  44. 'PTS'
  45. 'MAOA'
  46. 'HAO2'
  47. 'HSD17B7'
  48. 'MCEE'
  49. 'ACAT2'
  50. 'AUH'
  51. 'HPGD'
  52. 'FH'
  53. 'HMGCS2'
  54. 'ALAD'
  55. 'GPD1'
  56. 'ACO2'
  57. 'CBR1'
  58. 'GRHPR'
  59. 'ACOT2'
  60. 'G0S2'
  61. 'MDH2'
  62. 'HSP90AA1'
  63. 'BCKDHB'
  64. 'UROS'
  65. 'YWHAH'
  66. 'LDHA'
  67. 'CRYZ'
  68. 'RDH16'
  69. 'INMT'
  70. 'UGDH'
  71. 'GSTZ1'
  72. 'IDH3B'
  73. 'MDH1'
  74. 'CRAT'
  75. 'ETFDH'
  76. 'CD36'
  77. 'ECI2'
  78. 'SDHD'
  79. 'ACSL5'
  80. 'HSDL2'
  81. 'HMGCS1'
  82. 'SDHC'
  83. 'CD1D'
  84. 'GCDH'
  85. 'GPD2'
  86. 'ALDH3A1'
  87. 'SLC22A5'
  88. 'PDHB'
  89. 'TDO2'
  90. 'FASN'
  91. 'NBN'
  92. 'PSME1'
  93. 'PPARA'
  94. 'NCAPH2'
  95. 'BPHL'
  96. 'ODC1'
  97. 'CA6'
  98. 'DLD'
  99. 'HCCS'
  100. 'EPHX1'
  101. 'DLST'
  102. 'FMO1'
  103. 'AOC3'
  104. 'UROD'
  105. 'CPT1A'
  106. 'KMT5A'
  107. 'UBE2L6'
  108. 'MIF'
  109. 'SUCLG2'
  110. 'CPOX'
  111. 'SMS'
  112. 'CBR3'
  113. 'NTHL1'
  114. 'CIDEA'
  115. 'IDI1'
  116. 'AADAT'
  117. 'REEP6'
  118. 'HSPH1'
  119. 'APEX1'
  120. 'NSDHL'
  121. 'ADIPOR2'
  122. 'ACSL4'
  123. 'ACSS1'
  124. 'ENO3'
  125. 'IDH3G'
  126. 'LGALS1'
  127. 'METAP1'
  128. 'ALDOA'
  129. 'ACSM3'
  130. 'LTC4S'
  131. 'D2HGDH'
  132. 'ADSL'
  133. 'SUCLA2'
  134. 'SDHA'
  135. 'XIST'
  136. 'OSTC'
  137. 'GLUL'
  138. 'CYP4A22'
  139. 'GABARAPL1'
  140. 'AQP7'
  141. 'CYP1A1'
  142. 'PRDX6'
  143. 'ERP29'
  144. 'H2AFZ'
  145. 'GAPDHS'
  146. 'DHCR24'
  147. 'GAD2'
  148. 'PTPRG'
  149. 'IL4I1'
  150. 'TP53INP2'
  151. 'PDHA1'
  152. 'RAP1GDS1'
  153. 'CEL'
  154. 'BLVRA'
  155. 'SERINC1'
  156. 'BMPR1B'
  157. 'RDH11'
  158. 'ENO2'
  159. NA
  160. NA
  161. NA
  162. NA
  163. NA
  164. NA
  165. NA
  166. NA
  167. NA
  168. NA
  169. NA
  170. NA
  171. NA
  172. NA
  173. NA
  174. NA
  175. NA
  176. NA
  177. NA
  178. NA
  179. NA
  180. NA
  181. NA
  182. NA
  183. NA
  184. NA
  185. NA
  186. NA
  187. NA
  188. NA
  189. NA
  190. NA
  191. NA
  192. NA
  193. NA
  194. NA
  195. NA
  196. NA
  197. NA
  198. NA
  199. NA
  200. NA
In [293]:
ggplot(data = plot_df)+
geom_boxplot(aes(x=variable,y=value,fill=pw))+
theme_classic()
Warning message:
“Removed 408 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
No description has been provided for this image
In [294]:
ggplot(data = plot_df)+
geom_violin(aes(x=variable,y=value,fill=pw))+
theme_classic()
Warning message:
“Removed 408 rows containing non-finite outside the scale range
(`stat_ydensity()`).”
No description has been provided for this image
In [325]:
plot_df$pw <- plot_df$pw %>% as.character %>% factor(levels = c('TRUE','FALSE'),labels = c('In two pathway','Out of two pathway'))
In [373]:
#ggplot2 | position_dodge位置调整函数https://blog.csdn.net/weixin_54000907/article/details/120108707
box_p <- ggplot(data = plot_df)+
stat_boxplot(aes(x=variable,y=value,color = pw),geom = "errorbar",linewidth=1.5,
               position = position_dodge2(padding = 0.2))+
geom_boxplot(aes(x=variable,y=value,color = pw),outliers = F,linewidth=1.5,fill='white',position = position_dodge2(padding = 0.2))+
#geom_jitter(aes(x=variable,y=value,color = pw),width = 0.15)+
theme_classic()+
xlab('Run with different seeds')+
ylab('SHAP weight of driver genes')+
labs(fill="Genes Type")+
theme_bw()+
scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5),
    axis.title.y = element_text(vjust = 6),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(angle = 60,vjust = 0.5),
    #axis.text.y = element_text(hjust = 8),
    axis.ticks = element_line(linewidth = 1.5),
   axis.ticks.length = unit(10,'points'),
   axis.line = element_line(linewidth = 1.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   panel.border = element_blank(),
   panel.grid = element_blank(),
   #panel.grid.major.x =element_line(),
   legend.key.size = unit(20,'points'),
   legend.text = element_text(face = 'italic',size=18),
   #legend.title = element_text(face = 'bold',size=18), 
   legend.title = element_blank(),
   legend.position = 'top'
      
)
box_p
Warning message:
“Removed 408 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Warning message:
“Removed 408 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
No description has been provided for this image
In [374]:
ggsave(plot = box_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_box_p.pdf',
       width =16/1.5, height =16/1.5)
ggsave(plot = box_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_box_p.png',
       width =16/1.5, height =16/1.5)
Warning message:
“Removed 408 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Warning message:
“Removed 408 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Warning message:
“Removed 408 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Warning message:
“Removed 408 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
In [375]:
gl <- drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:10]
In [376]:
tmp <- plot_df[plot_df$Gene %in% gl,]

tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [377]:
gl
  1. 'MYL6'
  2. 'FN1'
  3. 'NQO1'
  4. 'GSTM3'
  5. 'PRDX1'
  6. 'LAPTM4A'
  7. 'TGM2'
  8. 'UBE2S'
  9. 'MDK'
  10. 'CD9'
In [380]:
?ggsci::scale_fill_npg()
In [402]:
ridge_p <- ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
  ggridges::geom_density_ridges() +
  ggridges::theme_ridges() + 
  ggsci::scale_fill_npg()+
xlab('SHAP weight')+
ylab('Driver genes')+
ggtitle("Weight through model with different seeds")+
scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   legend.position = 'none'
      
)
ridge_p
Picking joint bandwidth of 0.000309

Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
No description has been provided for this image
In [403]:
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_ridge_p.pdf',
       width =16/1.5, height =16/1.5)
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_ridge_p.png',
       width =16/1.5, height =16/1.5)
Picking joint bandwidth of 0.000309

Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
Picking joint bandwidth of 0.000309

Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [404]:
normalize <- function(v) {
  (v - min(v)) / (max(v) - min(v))
}
In [405]:
drivers$norm_shap <-  normalize(drivers$weight_shap_total_mean)
ddf <- drivers[order(drivers$weight_shap_total_mean,decreasing = T),][1:10,] %>% as.data.frame()
ddf$V1 <- factor(ddf$V1,levels = ddf$V1)
In [408]:
ggplot(ddf) +
  geom_bar(aes(x = V1, y = norm_shap),stat = "identity",fill=rgb(0.1,0.4,0.5,0.7))+
    theme_classic()
No description has been provided for this image
In [414]:
drivers$norm_shap <-  normalize(drivers$weight_shap_total_mean)
ddf <- drivers[order(drivers$weight_shap_total_mean,decreasing = T),][1:10,] %>% as.data.frame()
ddf$V1 <- factor(ddf$V1,levels = rev(ddf$V1))
In [436]:
bar_p <-  ggplot(ddf) +
  geom_bar(aes(x = V1, y = norm_shap,fill=norm_shap),stat = "identity",width = 0.6, alpha=1)+
    scale_fill_gradient(low = "#659999",high = "#f4791f")+
    coord_flip() +
    theme_bw()+
    ylab('SHAP weight')+
    xlab('Driver genes')+
    ggtitle("Modeling between T0 T14")+
    #scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
    theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5,hjust = 0.5),
        axis.title.y = element_text(vjust = 5,hjust = 0.5),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
       axis.text.x = element_text(vjust = 0.5),
       axis.text.y = element_text(colour = c(rep('black',times = 5),'#BA002B','black','#BA002B',rep('black',times = 2))),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
        panel.border = element_rect(size = 1.5),
        panel.grid = element_blank(),
        panel.grid.minor.x = element_line(linetype = 5),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(10,'points'),
       legend.position = 'none'

    )
bar_p
Warning message:
“Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.”
No description has been provided for this image
In [439]:
ggsave(plot = bar_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_bar_p.pdf',
       width =12/1.5, height =16/1.5)
ggsave(plot = bar_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_bar_p.png',
       width =12/1.5, height =16/1.5)
In [ ]:
#R语言ggplot2图例标签、标题、顺序修改和删除https://blog.csdn.net/LeaningR/article/details/114576555
#ggplot2 美化 背景/主题(theme)https://zhuanlan.zhihu.com/p/463041897
p <- ggplot(data = plot_df)+
geom_bar(aes(x=ID,y=enrichmentScore,fill=-log10(pvalue)),stat="identity",width = 0.6, alpha=0.85)+
#ggsci::scale_fill_gsea()+
scale_fill_gradient(low = "#659999",high = "#f4791f")+
#scale_fill_gradient(low = "#8360c3",high = "#2ebf91")+
#scale_fill_gradient(low = "#636363",high = "#a2ab58")+
#scale_fill_gradient2(low = "#355C7D",mid = "#6C5B7B",high = "#C06C84",midpoint = 1.6)+
coord_flip() +
xlab('Hallmark gene sets')+
ylab('Enrichment Score')+
labs(fill = "-log10(P)")+
theme_bw()+
theme(panel.border = element_rect(size = 1.5),
      panel.grid = element_blank(),
      axis.ticks = element_line(size = 1),
      axis.ticks.length = unit(5,'points'),
      axis.text = element_text(face = 'bold',colour = 'black'),
      axis.text.y = element_text(colour = c(rep('black',times = 4),'#BA002B','#BA002B')),
      #axis.title.x = element_text(),
      axis.title.y = element_blank()
      )

p
In [175]:
for (i in drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:25]){
    message(i)
}
MYL6

FN1

NQO1

GSTM3

PRDX1

LAPTM4A

TGM2

UBE2S

MDK

CD9

FOSL1

KRT8

CTSA

HIST1H2BD

ALPP

HIST1H4C

TFDP1

CSTB

MT2A

PGK1

SQSTM1

CD24

RANBP1

CD81

CKS1B

In [5]:
drivers_exp <- fread(file.path(read_dir,'driver_summary_shap_total_addexp.csv'))
In [6]:
head(drivers_exp)
A data.table: 6 × 13
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwaytime0_exptime3_exptime7_exptime14_exp
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl><dbl><dbl>
MYL6 0.0085084580.00084629550.000168211310FALSEFALSEFALSEFALSE-1.2952663-0.1738619-0.54296390.5844932
FN1 0.0079933780.00075376070.000190769510FALSEFALSEFALSEFALSE-0.7126312-0.2556758-0.07747290.3388334
NQO1 0.0071891720.00058341660.000174598810FALSEFALSE TRUE TRUE-0.4633937-0.6412671-0.39414140.5786879
GSTM3 0.0065702270.00047768010.000147023410FALSEFALSEFALSEFALSE-0.4995057-0.4245171-0.55196540.5208334
PRDX1 0.0057532930.00059483050.000130708310FALSEFALSE TRUE TRUE-0.7079641-0.5385526-0.64592650.6627243
LAPTM4A0.0055573910.00069891030.000119196610FALSEFALSEFALSEFALSE-1.1327481-0.2266731-0.45346260.5443161
In [210]:
drivers_exp_long<-drivers_exp %>%as.data.frame %>% 
    mutate(direction=ifelse(weight_grad_total_dir_mean > 0,'pos','neg')) %>%
    mutate(rank=rank(-drivers_exp$weight_shap_total_mean)) %>%
    mutate(top20 = ifelse(rank<=20,TRUE,FALSE)) %>%
    mutate(top10 = ifelse(rank<=10,TRUE,FALSE)) %>%
    dplyr::select(c('V1','time0_exp','time3_exp','time7_exp','time14_exp','direction','top20','top10')) %>%
    reshape2::melt(id.vars = c("V1",'direction','top20','top10'), #需保留的不参与聚合的变量列名
                  measure.vars = c('time0_exp','time3_exp','time7_exp','time14_exp'),#需要聚合的变量s1-s10
                  variable.name = c('time_point'),#聚合变量的新列名
                  value.name = 'exp')#聚合值的新列名
In [211]:
head(drivers_exp_long)
A data.frame: 6 × 6
V1directiontop20top10time_pointexp
<chr><chr><lgl><lgl><fct><dbl>
1MYL6 posTRUETRUEtime0_exp-1.2952663
2FN1 posTRUETRUEtime0_exp-0.7126312
3NQO1 posTRUETRUEtime0_exp-0.4633937
4GSTM3 posTRUETRUEtime0_exp-0.4995057
5PRDX1 posTRUETRUEtime0_exp-0.7079641
6LAPTM4AposTRUETRUEtime0_exp-1.1327481
In [9]:
p<-ggplot(drivers_exp_long,aes(x=time_point,y=exp,group = V1,color = direction))+
  ggalt::geom_xspline(spline_shape = -0.5)+
  #geom_line()+
  geom_point(size=3)+
  theme_bw()+
    ggsci::scale_colour_npg()
p
Registered S3 methods overwritten by 'ggalt':
  method                  from   
  grid.draw.absoluteGrob  ggplot2
  grobHeight.absoluteGrob ggplot2
  grobWidth.absoluteGrob  ggplot2
  grobX.absoluteGrob      ggplot2
  grobY.absoluteGrob      ggplot2

Warning message:
“Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.”
No description has been provided for this image
In [213]:
pos_drivers <- drivers_exp_long %>% filter(direction=='pos')
neg_drivers <- drivers_exp_long %>% filter(direction=='neg')
In [11]:
p2<-ggplot(pos_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
  ggalt::geom_xspline(spline_shape = -0.5)+
  ggalt::geom_xspline(data = pos_drivers%>% filter(top20),spline_shape = -0.5)+
  geom_line()+
  geom_point(size=3)+
  geom_point(data = pos_drivers%>% filter(top20),size=3)+
  theme_bw()+
  scale_color_manual(values = c('TRUE'= '#D94032FF','FALSE'='#00000033'))

p2
No description has been provided for this image
In [29]:
p2<-ggplot(pos_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
  ggbump::geom_bump(smooth = 4)+
  ggbump::geom_bump(data = pos_drivers%>% filter(top20),smooth = 4)+
  #geom_line()+
  geom_point(size=3)+
  geom_point(data = pos_drivers%>% filter(top20),size=3)+
  theme_bw()+
  scale_color_manual(values = c('TRUE'= '#D94032FF','FALSE'='#00000033'))

p2
No description has been provided for this image
In [32]:
ggsave(plot = p2,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pos_driver_cross_time_ggbump.pdf',width =10, height =5)
ggsave(plot = p2,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pos_driver_cross_time_ggbump.png',width =10, height =5)
In [17]:
#https://github.com/hrbrmstr/ggalt/issues/60
library(ggalt)
GeomXSpline <- ggplot2::ggproto("GeomXSpline", ggplot2::Geom,
                        required_aes = c("x", "y"),
                        default_aes = ggplot2::aes(colour = "black",
                                                   size = 0.5, 
                                                   linetype = 1,
                                                   alpha = 1, 
                                                   spline_shape=-1,
                                                   open=T),

                        draw_key = ggplot2::draw_key_smooth, # controls what is drawn in legend

                        draw_group = function(data, panel_params, coord) {

                          n <- nrow(data)
                          if (n <= 2) return(grid::nullGrob())

                          coords <- coord$transform(data, panel_params)

                          first_row <- coords[1, , drop = FALSE]

                          grid::xsplineGrob(
                            coords$x, coords$y,
                            shape = coords$spline_shape,
                            open = coords$open[1],
                            gp = grid::gpar(col = first_row$colour,
                                            lwd = first_row$size * .pt,
                                            alpha = first_row$alpha,
                                            lty = first_row$linetype)
                          )
                        }
)

geom_xspline <- function(mapping = NULL,
                         data = NULL,
                         stat = "identity",
                         position = "identity",
                         spline_shape = -1,
                         open = T,
                         na.rm = FALSE,
                         show.legend = NA,
                         inherit.aes = TRUE,
                         ...) {
  layer(
    geom = GeomXSpline,
    mapping = mapping,
    data = data,
    stat = stat,
    position = position,
    show.legend = show.legend,
    inherit.aes = inherit.aes,
    params = list(spline_shape = spline_shape,
                  open = open,
                  na.rm = na.rm,
                  ...)
  )
}
In [37]:
pos_drivers%>% filter(top20) %>% subset(time_point=='time0_exp')
A data.frame: 13 × 5
V1directiontop20time_pointexp
<chr><chr><lgl><fct><dbl>
1MYL6 posTRUEtime0_exp-1.2952663
2FN1 posTRUEtime0_exp-0.7126312
3NQO1 posTRUEtime0_exp-0.4633937
4GSTM3 posTRUEtime0_exp-0.4995057
5PRDX1 posTRUEtime0_exp-0.7079641
6LAPTM4A posTRUEtime0_exp-1.1327481
7MDK posTRUEtime0_exp-1.1022027
8CD9 posTRUEtime0_exp-1.2298415
9KRT8 posTRUEtime0_exp-0.7827570
10CTSA posTRUEtime0_exp-1.0257106
11HIST1H2BDposTRUEtime0_exp-0.5420957
12ALPP posTRUEtime0_exp-0.5967594
13PGK1 posTRUEtime0_exp-0.4168932
In [271]:
#修改 R 中的 ggplot X 轴刻度标签https://www.delftstack.com/zh/howto/r/ggplot-axis-tick-labels-in-r/
#如何修改坐标轴的刻度间隔:https://blog.csdn.net/qq_42458954/article/details/112604443
p2<-ggplot(pos_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
  geom_xspline(spline_shape = -0.5)+
  geom_xspline(data = pos_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
  ggrepel::geom_text_repel(data=pos_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
                  aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
  geom_point(size=3)+
  geom_point(data = pos_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
  xlab('Time points (Days)')+
  ylab('Normalized expression')+
  ggtitle('Time-course gene expression')+
  theme_bw()+
  scale_color_manual(values = c('TRUE'= '#259CA2BB','FALSE'='#00000033'))+
  scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
  #ylim(-1.5,1.5)+
  scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.6,1.6))+
  theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5),
        axis.title.y = element_text(vjust = 6),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
       axis.text.x = element_text(vjust = -2),
        axis.text.y = element_text(hjust = -1.5),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(-10,'points'),
       axis.line = element_line(linewidth = 1),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
       panel.border = element_rect(linewidth = 1.5),
       panel.grid = element_blank(),
       #panel.grid.major.x =element_line(),
       legend.position = 'None')


p2
No description has been provided for this image
In [272]:
p3<-ggplot(data = pos_drivers%>% filter(top20),aes(x=time_point,y=exp,group = V1))+
  geom_xspline(spline_shape = -0.5,size=1.5,color= '#259CA2AA')+
  ggrepel::geom_text_repel(data=pos_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
                  aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black',color= '#259CA2FF')+
  geom_point(data = pos_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5,color= '#259CA2FF')+
  scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
  scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.6,1.6))+
  xlab('Time points (Days)')+
  ylab('Normalized expression')+
  ggtitle('Time-course gene expression')+
  theme_bw()+
  theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5),
        axis.title.y = element_text(vjust = 6),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
       axis.text.x = element_text(vjust = -2),
        axis.text.y = element_text(hjust = -1.5),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(-10,'points'),
       axis.line = element_line(linewidth = 1),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
       panel.border = element_rect(linewidth = 1.5),
       panel.grid = element_blank(),
       #panel.grid.major.x =element_line(),
       legend.position = 'None')


p3
No description has been provided for this image
In [273]:
ggsave(plot = p2,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pos_driver_cross_time_ggalt.pdf',
       width =16/1.5, height =9/1.5)
ggsave(plot = p2,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pos_driver_cross_time_ggalt.png',
       width =16/1.5, height =9/1.5)

ggsave(plot = p3,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pos_driver_cross_time_ggalt_only.pdf',
       width =16/1.5, height =9/1.5)
ggsave(plot = p3,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pos_driver_cross_time_ggalt_only.png',
       width =16/1.5, height =9/1.5)
In [275]:
p4<-ggplot(neg_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
  geom_xspline(spline_shape = -0.5)+
  geom_xspline(data = neg_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
  ggrepel::geom_text_repel(data=neg_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
                  aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
  geom_point(size=3)+
  geom_point(data = neg_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
  xlab('Time points (Days)')+
  ylab('Normalized expression')+
  ggtitle('Time-course gene expression')+
  theme_bw()+
  scale_color_manual(values = c('TRUE'= '#E0A965BB','FALSE'='#00000033'))+
  scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
  scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.7,1.7))+
  theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5),
        axis.title.y = element_text(vjust = 6),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
       axis.text.x = element_text(vjust = -2),
        axis.text.y = element_text(hjust = -1.5),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(-10,'points'),
       axis.line = element_line(linewidth = 1),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
       panel.border = element_rect(linewidth = 1.5),
       panel.grid = element_blank(),
       #panel.grid.major.x =element_line(),
       legend.position = 'None')


p4
No description has been provided for this image
In [276]:
p5<-ggplot(data = neg_drivers%>% filter(top20),aes(x=time_point,y=exp,group = V1))+
  geom_xspline(spline_shape = -0.5,size=1.5,color= '#E0A965AA')+
  ggrepel::geom_text_repel(data=neg_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
                  aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black',color= '#E0A965FF')+
  geom_point(data = neg_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5,color= '#E0A965FF')+
  scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
  scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.7,1.7))+
  xlab('Time points (Days)')+
  ylab('Normalized expression')+
  ggtitle('Time-course gene expression')+
  theme_bw()+
  theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5),
        axis.title.y = element_text(vjust = 6),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
       axis.text.x = element_text(vjust = -2),
        axis.text.y = element_text(hjust = -1.5),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(-10,'points'),
       axis.line = element_line(linewidth = 1),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
       panel.border = element_rect(linewidth = 1.5),
       panel.grid = element_blank(),
       #panel.grid.major.x =element_line(),
       legend.position = 'None')


p5
No description has been provided for this image
In [277]:
ggsave(plot = p4,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_neg_driver_cross_time_ggalt.pdf',
       width =16/1.5, height =9/1.5)
ggsave(plot = p4,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_neg_driver_cross_time_ggalt.png',
       width =16/1.5, height =9/1.5)

ggsave(plot = p5,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_neg_driver_cross_time_ggalt_only.pdf',
       width =16/1.5, height =9/1.5)
ggsave(plot = p5,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_neg_driver_cross_time_ggalt_only.png',
       width =16/1.5, height =9/1.5)
In [440]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
'select()' returned 1:1 mapping between keys and columns

Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“6.35% of input gene IDs are fail to map...”
In [468]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>% 
  dplyr::select(gs_name, entrez_gene)

m_t2g$gs_name <- m_t2g$gs_name %>% 
    stringr::str_replace_all(pattern = 'HALLMARK_',replacement = '') %>%
    stringr::str_replace_all(pattern = '_',replacement = ' ') %>%
    stringr::str_to_title()

kk <- enrichKEGG(gene         = names(gs),
                 #keyType      = 'uniprot',
                 organism     = 'hsa',
                 pvalueCutoff = 0.05)
#head(kk)
em <- enricher(names(gs), 
           TERM2GENE=m_t2g,
          minGSSize    = 0,
              #maxGSSize    = 500,
              pvalueCutoff = 1,
              #scoreType = "pos"
              qvalueCutoff = 1,
              #eps = eps,
              #verbose      = FALSE
              )
In [485]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()

for (i in 1:nrow(em@result)){
    if (i==1){
        
        ls <- em@geneSets[em@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
        
    }else{
        
    ls <- em@geneSets[em@result[i,'ID']]
    where <- which(high_df$ENTREZID %in% ls[[1]])
    tmp <- high_df[where,]
    tmp$gs <- gs_name[i]
    tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
    high_plot_df <- rbind(high_plot_df,tmp)
    high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
        
    }
}

high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [486]:
#ggplot2 修改图例的一些操作 https://zhuanlan.zhihu.com/p/166529941
pRidge_H <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = 'Hallmark',fill = 'Log10 mean of weight') +
    xlab('SHAP weight')+
    ylab('Gene set')+
  scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
  scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18), 
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
       )

pRidge_H
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.487

No description has been provided for this image
In [487]:
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pRidge_H.pdf',
       width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pRidge_H.png',
       width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.487

Picking joint bandwidth of 0.487

In [488]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- kk@result[1:30,]$Description
high_mean_ls <-c()

for (i in 1:nrow(kk@result[1:30,])){
    if (i==1){
        
        ls <- kk@geneSets[kk@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
        
    }else{
        
    ls <- kk@geneSets[kk@result[i,'ID']]
    where <- which(high_df$ENTREZID %in% ls[[1]])
    tmp <- high_df[where,]
    tmp$gs <- gs_name[i]
    tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
    high_plot_df <- rbind(high_plot_df,tmp)
    high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
        
    }
}

high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [489]:
pRidge_K <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = 'Hallmark',fill = 'Log10 mean of weight') +
    xlab('SHAP weight')+
    ylab('Gene set')+
  scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
  scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = 'white'), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18), 
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
       )

pRidge_K
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.4

No description has been provided for this image
In [490]:
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pRidge_K.pdf',
       width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pRidge_K.png',
       width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.4

Picking joint bandwidth of 0.4

In [785]:
increase_control_details0 <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/increase_control_details.csv')
head(increase_control_details0)
A data.table: 6 × 7
V1sample_idxsample_namescoreprob_deltacausal_deltan_iter
<int><int><chr><dbl><dbl><dbl><int>
000-03.064079e-033.980619e-022.45427750199
111-04.584978e-045.650461e-032.32819680199
222-03.064079e-033.980619e-022.45427750199
333-09.449210e-031.251141e-012.50140500199
444-04.479431e-027.928820e-013.34394260199
550-11.336331e-054.768372e-070.01488078 11
In [786]:
increase_control_details7 <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t7_t14_cyc/increase_control_details.csv')
head(increase_control_details7)
A data.table: 6 × 7
V1sample_idxsample_namescoreprob_deltacausal_deltan_iter
<int><int><chr><dbl><dbl><dbl><int>
000-00.022516340.97872408.211747e+00199
111-00.027124800.98687646.873363e+00199
222-00.022516340.97872408.211747e+00199
333-00.022855870.98611298.150834e+00199
444-00.026386040.98346717.041394e+00199
550-10.000000000.00000001.304077e-07 11
In [787]:
increase_control_details0$group = 'T0 to T14'
increase_control_details7$group = 'T7 to T14'
In [788]:
increase_control_details <- rbind(increase_control_details0[1:5,],increase_control_details7[1:5,])
In [789]:
increase_control_details
A data.table: 10 × 8
V1sample_idxsample_namescoreprob_deltacausal_deltan_itergroup
<int><int><chr><dbl><dbl><dbl><int><chr>
000-00.00306407930.0398061872.454277199T0 to T14
111-00.00045849780.0056504612.328197199T0 to T14
222-00.00306407930.0398061872.454277199T0 to T14
333-00.00944920970.1251141432.501405199T0 to T14
444-00.04479430890.7928820103.343943199T0 to T14
000-00.02251633790.9787239868.211747199T7 to T14
111-00.02712479890.9868763566.873363199T7 to T14
222-00.02251633790.9787239868.211747199T7 to T14
333-00.02285586620.9861129058.150834199T7 to T14
444-00.02638604310.9834670927.041394199T7 to T14
In [790]:
wilcox.test(score~group,data=increase_control_details,alternative = 'less',exact = F)
	Wilcoxon rank sum test with continuity correction

data:  score by group
W = 5, p-value = 0.07062
alternative hypothesis: true location shift is less than 0
In [791]:
#Kolmogorov-Smirnov test.
stats::ks.test(score~group,data=increase_control_details,alternative = 'greater',exact = F)
Warning message in ks.test.default(x = DATA[[1L]], y = DATA[[2L]], ...):
“p-value will be approximate in the presence of ties”
	Asymptotic two-sample Kolmogorov-Smirnov test

data:  score by group
D^+ = 0.8, p-value = 0.04076
alternative hypothesis: the CDF of T0 to T14 lies above that of T7 to T14
In [792]:
controlp <- ggplot(data = increase_control_details,aes(x = group,y = score,fill= group))+
stat_boxplot(geom = "errorbar",linewidth=1.5)+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
                      comparisons =  list(c('T0 to T14','T7 to T14')),
                      annotations= c('P-value = 0.04076'),textsize = 8,size=1.5,vjust=0) +

theme_classic()+
xlab('Time point modeling')+
ylab('Status increasing control score')+
labs(fill="Genes Type")+
theme_bw()+
scale_fill_manual(values = c('T0 to T14'= '#559073FF','T7 to T14'='#D28130FF'))+
scale_color_manual(values = c('T0 to T14'= '#559073FF','T7 to T14'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5),
    axis.title.y = element_text(vjust = 6),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(angle = 60,vjust = 0.5),
    #axis.text.y = element_text(hjust = 8),
    axis.ticks = element_line(linewidth = 1.5),
   axis.ticks.length = unit(10,'points'),
   axis.line = element_line(linewidth = 1.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   panel.border = element_blank(),
   panel.grid = element_blank(),
   #panel.grid.major.x =element_line(),
   legend.key.size = unit(20,'points'),
   legend.text = element_text(face = 'italic',size=18),
   #legend.title = element_text(face = 'bold',size=18), 
   legend.title = element_blank(),
   legend.position = 'top')
controlp
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
No description has been provided for this image
In [793]:
ggsave(plot = controlp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_controlp.pdf',
       width =16/1.5, height =16/1.5)
ggsave(plot = controlp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_controlp.png',
       width =16/1.5, height =16/1.5)
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [775]:
decrease_control_details0 <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/decrease_control_details.csv')
tail(decrease_control_details0)
decrease_control_details7 <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t7_t14_cyc/decrease_control_details.csv')
tail(decrease_control_details7)

decrease_control_details0$group = 'T14 to T0'
decrease_control_details7$group = 'T14 to T7'

decrease_control_details <- rbind(decrease_control_details0[6:13,],decrease_control_details7[6:13,])
A data.table: 6 × 7
V1sample_idxsample_namescoreprob_deltacausal_deltan_iter
<int><int><chr><dbl><dbl><dbl><int>
7 72-10.02052324580.98098514 9.030036199
8 83-10.01400266160.9720405313.114353199
9 94-10.00016052630.00163569 1.924987199
10105-10.01619058770.9727472811.350383199
11116-10.01606663270.9647213311.343579199
12127-10.01463269220.9563103912.346610199
A data.table: 6 × 7
V1sample_idxsample_namescoreprob_deltacausal_deltan_iter
<int><int><chr><dbl><dbl><dbl><int>
7 72-1 NA0.000000e+00 0.0000000 11
8 83-18.516641e-081.192093e-07 0.5837294 11
9 94-1 NA0.000000e+00 0.0000000 11
10105-18.061132e-081.192093e-07 0.6167141 11
11116-11.311363e-029.920255e-0114.2913460199
12127-13.620758e-078.344650e-07 0.8985244 13
In [777]:
#Kolmogorov-Smirnov test.
stats::ks.test(score~group,data=decrease_control_details,alternative = 'less',exact = F)
Warning message in ks.test.default(x = DATA[[1L]], y = DATA[[2L]], ...):
“p-value will be approximate in the presence of ties”
	Asymptotic two-sample Kolmogorov-Smirnov test

data:  score by group
D^- = 0.875, p-value = 0.005248
alternative hypothesis: the CDF of T14 to T0 lies below that of T14 to T7
In [783]:
Dcontrolp <- ggplot(data = decrease_control_details,aes(x = group,y = score,fill= group))+
stat_boxplot(geom = "errorbar",linewidth=1.5)+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
                      comparisons =  list(c('T14 to T0','T14 to T7')),
                      annotations= c('P-value = 0.005248'),textsize = 8,size=1.5,vjust=0) +

theme_classic()+
xlab('Time point modeling')+
ylab('Status decreasing control score')+
labs(fill="Genes Type")+
theme_bw()+
scale_fill_manual(values = c('T14 to T0'= '#559073FF','T14 to T7'='#D28130FF'))+
scale_color_manual(values = c('T14 to T0'= '#559073FF','T14 to T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5),
    axis.title.y = element_text(vjust = 6),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(angle = 60,vjust = 0.5),
    #axis.text.y = element_text(hjust = 8),
    axis.ticks = element_line(linewidth = 1.5),
   axis.ticks.length = unit(10,'points'),
   axis.line = element_line(linewidth = 1.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   panel.border = element_blank(),
   panel.grid = element_blank(),
   #panel.grid.major.x =element_line(),
   legend.key.size = unit(20,'points'),
   legend.text = element_text(face = 'italic',size=18),
   #legend.title = element_text(face = 'bold',size=18), 
   legend.title = element_blank(),
   legend.position = 'top')
Dcontrolp
Warning message:
“Removed 2 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Warning message:
“Removed 2 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Warning message:
“Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
Warning message:
“Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).”
No description has been provided for this image
In [784]:
ggsave(plot = Dcontrolp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_Dcontrolp.pdf',
       width =16/1.5, height =16/1.5)
ggsave(plot = Dcontrolp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_Dcontrolp.png',
       width =16/1.5, height =16/1.5)
Warning message:
“Removed 2 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Warning message:
“Removed 2 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Warning message:
“Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
Warning message:
“Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).”
Warning message:
“Removed 2 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Warning message:
“Removed 2 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Warning message:
“Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
Warning message:
“Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).”
In [906]:
increase_control_details_t7Driver<- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_adata_increase_t7Driver.csv')
In [907]:
increase_control_details_t7Driver$group = c(rep('T0',times = 5),rep('T14',times = 8),rep('T7',times = 5))
In [913]:
#Kolmogorov-Smirnov test.
tmp1 <- increase_control_details_t7Driver[c(1:5,14:18),]
stats::ks.test(score~group,data=tmp1,alternative = 'greater',exact = F)
Warning message in ks.test.default(x = DATA[[1L]], y = DATA[[2L]], ...):
“p-value will be approximate in the presence of ties”
	Asymptotic two-sample Kolmogorov-Smirnov test

data:  score by group
D^+ = 0.8, p-value = 0.04076
alternative hypothesis: the CDF of T0 lies above that of T7
In [916]:
controlp <- ggplot(data = increase_control_details_t7Driver[c(1:5,14:18),],aes(x = group,y = score,fill= group))+
stat_boxplot(geom = "errorbar",linewidth=1.5)+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
                      comparisons =  list(c('T0','T7')),
                      annotations= c('P-value = 0.04076'),textsize = 8,size=1.5,vjust=0) +

theme_classic()+
xlab('Time point modeling')+
ylab('Status increasing control score')+
labs(fill="Genes Type")+
theme_bw()+
scale_fill_manual(values = c('T0'= '#559073FF','T7'='#D28130FF'))+
scale_color_manual(values = c('T0'= '#559073FF','T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5),
    axis.title.y = element_text(vjust = 6),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(angle = 60,vjust = 0.5),
    #axis.text.y = element_text(hjust = 8),
    axis.ticks = element_line(linewidth = 1.5),
   axis.ticks.length = unit(10,'points'),
   axis.line = element_line(linewidth = 1.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   panel.border = element_blank(),
   panel.grid = element_blank(),
   #panel.grid.major.x =element_line(),
   legend.key.size = unit(20,'points'),
   legend.text = element_text(face = 'italic',size=18),
   #legend.title = element_text(face = 'bold',size=18), 
   legend.title = element_blank(),
   legend.position = 'top')
controlp
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
No description has been provided for this image
In [917]:
increase_control_details_t0Driver<- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_adata_increase_t0Driver.csv')
In [921]:
increase_control_details_t0Driver$group = c(rep('T0',times = 5),rep('T14',times = 8),rep('T7',times = 5))
In [922]:
#Kolmogorov-Smirnov test.
tmp1 <- increase_control_details_t0Driver[c(1:5,14:18),]
stats::ks.test(score~group,data=tmp1,alternative = 'greater',exact = F)
Warning message in ks.test.default(x = DATA[[1L]], y = DATA[[2L]], ...):
“p-value will be approximate in the presence of ties”
	Asymptotic two-sample Kolmogorov-Smirnov test

data:  score by group
D^+ = 0.8, p-value = 0.04076
alternative hypothesis: the CDF of T0 lies above that of T7
In [923]:
controlp <- ggplot(data = increase_control_details_t7Driver[c(1:5,14:18),],aes(x = group,y = score,fill= group))+
stat_boxplot(geom = "errorbar",linewidth=1.5)+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
                      comparisons =  list(c('T0','T7')),
                      annotations= c('P-value = 0.04076'),textsize = 8,size=1.5,vjust=0) +

theme_classic()+
xlab('Time point modeling')+
ylab('Status increasing control score')+
labs(fill="Genes Type")+
theme_bw()+
scale_fill_manual(values = c('T0'= '#559073FF','T7'='#D28130FF'))+
scale_color_manual(values = c('T0'= '#559073FF','T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5),
    axis.title.y = element_text(vjust = 6),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(angle = 60,vjust = 0.5),
    #axis.text.y = element_text(hjust = 8),
    axis.ticks = element_line(linewidth = 1.5),
   axis.ticks.length = unit(10,'points'),
   axis.line = element_line(linewidth = 1.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   panel.border = element_blank(),
   panel.grid = element_blank(),
   #panel.grid.major.x =element_line(),
   legend.key.size = unit(20,'points'),
   legend.text = element_text(face = 'italic',size=18),
   #legend.title = element_text(face = 'bold',size=18), 
   legend.title = element_blank(),
   legend.position = 'top')
controlp
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
No description has been provided for this image
In [937]:
tmp <- rbind(increase_control_details_t0Driver[c(1:5),],increase_control_details_t7Driver[c(14:18),])

#Kolmogorov-Smirnov test.
stats::ks.test(score~group,data=tmp,alternative = 'greater',exact = F)
Warning message in ks.test.default(x = DATA[[1L]], y = DATA[[2L]], ...):
“p-value will be approximate in the presence of ties”
	Asymptotic two-sample Kolmogorov-Smirnov test

data:  score by group
D^+ = 0.8, p-value = 0.04076
alternative hypothesis: the CDF of T0 lies above that of T7
In [938]:
controlp <- ggplot(data = tmp,aes(x = group,y = score,fill= group))+
stat_boxplot(geom = "errorbar",linewidth=1.5)+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
                      comparisons =  list(c('T0','T7')),
                      annotations= c('P-value = 0.04076'),textsize = 8,size=1.5,vjust=0) +

theme_classic()+
xlab('Time point modeling')+
ylab('Status increasing control score')+
labs(fill="Genes Type")+
theme_bw()+
scale_fill_manual(values = c('T0'= '#559073FF','T7'='#D28130FF'))+
scale_color_manual(values = c('T0'= '#559073FF','T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5),
    axis.title.y = element_text(vjust = 6),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(angle = 60,vjust = 0.5),
    #axis.text.y = element_text(hjust = 8),
    axis.ticks = element_line(linewidth = 1.5),
   axis.ticks.length = unit(10,'points'),
   axis.line = element_line(linewidth = 1.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   panel.border = element_blank(),
   panel.grid = element_blank(),
   #panel.grid.major.x =element_line(),
   legend.key.size = unit(20,'points'),
   legend.text = element_text(face = 'italic',size=18),
   #legend.title = element_text(face = 'bold',size=18), 
   legend.title = element_blank(),
   legend.position = 'top')
controlp
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
No description has been provided for this image
In [939]:
ggsave(plot = controlp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_7_controlp.pdf',
       width =16/1.5, height =16/1.5)
ggsave(plot = controlp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_7_controlp.png',
       width =16/1.5, height =16/1.5)
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [147]:
decrease_control_details_t7Driver<- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_adata_decrease_t7Driver.csv')
decrease_control_details_t0Driver<- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_adata_decrease_t0Driver.csv')
In [941]:
decrease_control_details_t7Driver$group = c(rep('T0',times = 5),rep('T14 T0',times = 8),rep('T7',times = 5))
decrease_control_details_t0Driver$group = c(rep('T0',times = 5),rep('T14 T7',times = 8),rep('T7',times = 5))
In [942]:
#Kolmogorov-Smirnov test.
tmp1 <- decrease_control_details_t7Driver[c(6:13),]
tmp2 <- decrease_control_details_t0Driver[c(6:13),]
tmp <- rbind(tmp1,tmp2)
stats::ks.test(score~group,data=tmp,alternative = 'greater',exact = F)
Warning message in ks.test.default(x = DATA[[1L]], y = DATA[[2L]], ...):
“p-value will be approximate in the presence of ties”
	Asymptotic two-sample Kolmogorov-Smirnov test

data:  score by group
D^+ = 0.625, p-value = 0.04394
alternative hypothesis: the CDF of T14 T0 lies above that of T14 T7
In [943]:
Dcontrolp <- ggplot(data = tmp,aes(x = group,y = score,fill= group))+
#stat_boxplot(geom = "errorbar",linewidth=1.5)+
#geom_violin(outliers = F,linewidth=1.5,color='black')+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
                      comparisons =  list(c('T14 T0','T14 T7')),
                      annotations= c('P-value = 0.04394'),textsize = 8,size=1.5,vjust=0) +

theme_classic()+
xlab('Time point modeling')+
ylab('Status increasing control score')+
labs(fill="Genes Type")+
theme_bw()+
scale_fill_manual(values = c('T14 T0'= '#559073FF','T14 T7'='#D28130FF'))+
scale_color_manual(values = c('T14 T0'= '#559073FF','T14 T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5),
    axis.title.y = element_text(vjust = 6),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(angle = 60,vjust = 0.5),
    #axis.text.y = element_text(hjust = 8),
    axis.ticks = element_line(linewidth = 1.5),
   axis.ticks.length = unit(10,'points'),
   axis.line = element_line(linewidth = 1.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   panel.border = element_blank(),
   panel.grid = element_blank(),
   #panel.grid.major.x =element_line(),
   legend.key.size = unit(20,'points'),
   legend.text = element_text(face = 'italic',size=18),
   #legend.title = element_text(face = 'bold',size=18), 
   legend.title = element_blank(),
   legend.position = 'top')
Dcontrolp
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
No description has been provided for this image
In [944]:
ggsave(plot = Dcontrolp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_7_Dcontrolp.pdf',
       width =16/1.5, height =16/1.5)
ggsave(plot = Dcontrolp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_7_Dcontrolp.png',
       width =16/1.5, height =16/1.5)
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [ ]:

In [170]:
diff_gene_exp_meatcell4 <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/diff_gene_exp_meatcell4.csv')
In [171]:
head(diff_gene_exp_meatcell4)
A data.table: 6 × 5
V1GeneFDRpvalslogFC
<int><chr><dbl><dbl><dbl>
0HIST1H1D4.939636e-192.228474e-236.8936324
1HIST1H4C5.657670e-045.104818e-081.2869049
2YBX1 2.927308e-023.961889e-060.3632599
3MALAT1 1.794645e-013.238555e-050.7508955
4CTNNAL1 1.000000e+008.934546e-040.7036244
5NFE2L3 1.000000e+001.184413e-031.0585847
In [172]:
diff_gene_exp_meatcell4 <- diff_gene_exp_meatcell4 %>%mutate(change = as.factor(ifelse(pvals < 0.05 & abs(logFC) > 1.2,
                                   ifelse(logFC > 1 ,'Up','Down'),'No change')))
In [155]:
dim(diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Down',])
  1. 10
  2. 6
In [156]:
diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Down',]
A data.table: 10 × 6
V1GeneFDRpvalslogFCchange
<int><chr><dbl><dbl><dbl><fct>
22133ALKBH7 10.039087293 -1.752925Down
22139P2RY6 10.031032151-27.488358Down
22144KRTCAP210.026946023 -1.358575Down
22146ACO2 10.022090860 -2.060025Down
22148AIP 10.020344494 -2.571658Down
22150CHURC1 10.018870283 -1.314827Down
22151PSAP 10.016376696 -1.446578Down
22154NELFCD 10.014387572 -1.708666Down
22158SVBP 10.011970509 -1.389756Down
22161C6orf1 10.008870535 -1.309893Down
In [157]:
dim(diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Up',])
  1. 18
  2. 6
In [158]:
diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Up',]
A data.table: 18 × 6
V1GeneFDRpvalslogFCchange
<int><chr><dbl><dbl><dbl><fct>
0HIST1H1D4.939636e-192.228474e-236.893632Up
1HIST1H4C5.657670e-045.104818e-081.286905Up
6SUZ12 1.000000e+001.600379e-031.526876Up
14COX19 1.000000e+008.342897e-031.276946Up
15DAG1 1.000000e+008.948297e-031.372644Up
19VSTM1 1.000000e+001.291202e-021.842837Up
20BRCA1 1.000000e+001.300847e-021.459212Up
26ZFP91 1.000000e+001.543801e-021.565769Up
27CCND3 1.000000e+001.598086e-021.232958Up
29POLR3A 1.000000e+001.621440e-022.377943Up
30MECOM 1.000000e+001.801522e-021.957915Up
33CENPB 1.000000e+001.882000e-021.725816Up
45CNOT11 1.000000e+002.752299e-021.203864Up
53MAVS 1.000000e+003.008922e-021.467143Up
55LAS1L 1.000000e+003.123933e-021.597762Up
57VEZT 1.000000e+003.242696e-021.305980Up
64HKDC1 1.000000e+003.666902e-021.786746Up
80ZCCHC10 1.000000e+004.724312e-021.230941Up
In [159]:
diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$Gene == 'P2RY6','logFC'] = diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$Gene == 'P2RY6','logFC']+20
In [763]:
volcanop<- ggplot(diff_gene_exp_meatcell4 %>% filter(abs(logFC)<10),aes(logFC, -log10(pvals)))+
  geom_hline(yintercept = -log10(0.05), linetype = 5, color = "black",linewidth = 1)+
  geom_vline(xintercept = c(-1.2,1.2), linetype = 5, color = "black",linewidth = 1)+
  geom_point(aes(color = change),
             size = 4, 
             alpha = 0.75) +
  ggrepel::geom_text_repel(data =diff_gene_exp_meatcell4 %>% filter(abs(logFC)<10) %>% filter(change %in% c('Up','Down')),
                  max.overlaps = getOption("ggrepel.max.overlaps", default = 30),
                  # 这里的filter很关键,筛选你想要标记的基因
                  aes(label = Gene),
                  size = 5, box.padding = unit(2.5, "mm"),
                  color = 'black') +
  theme_bw(base_size = 12)+
  scale_color_manual(values = c('Up'='#0FB9F3','Down'='#EA5A18','No change'='#BBBBBB')) +
  theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5),
        axis.title.y = element_text(vjust = 6),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(10,'points'),
       axis.line = element_line(linewidth = 1),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
       panel.border = element_rect(linewidth = 1.5),
       panel.grid = element_blank(),
       #panel.grid.major.x =element_line(),
       legend.position = 'None')+
  xlab("Log2FC")+
  ylab("-Log10(P-value)")
volcanop
Warning message:
“ggrepel: 15 unlabeled data points (too many overlaps). Consider increasing max.overlaps”
No description has been provided for this image
In [764]:
ggsave(plot = volcanop,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_volcanop.pdf',
       width =14/1.5, height =16/1.5)
ggsave(plot = volcanop,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_volcanop.png',
       width =14/1.5, height =16/1.5)
In [772]:
diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Down',]$Gene %in% drivers$V1
  1. FALSE
  2. FALSE
  3. FALSE
  4. FALSE
  5. FALSE
  6. FALSE
  7. FALSE
  8. FALSE
  9. FALSE
  10. FALSE
In [174]:
up_0 <- diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Up',]$Gene
down_0 <- diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Down',]$Gene
In [175]:
diff_gene_exp_meatcell4 <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/diff_gene_exp14_meatcell4.csv')
In [176]:
diff_gene_exp_meatcell4 <- diff_gene_exp_meatcell4 %>%mutate(change = as.factor(ifelse(pvals < 0.05 & abs(logFC) > 1.2,
                                   ifelse(logFC > 1 ,'Up','Down'),'No change')))
In [183]:
volcanop<- ggplot(diff_gene_exp_meatcell4 %>% filter(abs(logFC)<10),aes(logFC, -log10(pvals)))+
  geom_hline(yintercept = -log10(0.05), linetype = 5, color = "black",linewidth = 1)+
  geom_vline(xintercept = c(-1.2,1.2), linetype = 5, color = "black",linewidth = 1)+
  geom_point(aes(color = change),
             size = 4, 
             alpha = 0.75) +
  ggrepel::geom_text_repel(data =diff_gene_exp_meatcell4 %>%
                           filter(abs(logFC)<10) %>% 
                           filter(change %in% c('Up','Down')) %>%
                           filter(-log10(pvals)>8 | abs(logFC) > 3),
                  max.overlaps = getOption("ggrepel.max.overlaps", default = 30),
                  # 这里的filter很关键,筛选你想要标记的基因
                  aes(label = Gene),
                  size = 5, box.padding = unit(2.5, "mm"),
                  color = 'black') +
  theme_bw(base_size = 12)+
  scale_color_manual(values = c('Up'='#0FB9F3','Down'='#EA5A18','No change'='#BBBBBB')) +
  theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5),
        axis.title.y = element_text(vjust = 6),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(10,'points'),
       axis.line = element_line(linewidth = 1),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
       panel.border = element_rect(linewidth = 1.5),
       panel.grid = element_blank(),
       #panel.grid.major.x =element_line(),
       legend.position = 'None')+
  xlab("Log2FC")+
  ylab("-Log10(P-value)")
volcanop
No description has been provided for this image
In [184]:
ggsave(plot = volcanop,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_volcanop_14low.pdf',
       width =14/1.5, height =16/1.5)
ggsave(plot = volcanop,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_volcanop_14low.png',
       width =14/1.5, height =16/1.5)
In [177]:
up_14 <- diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Up',]$Gene
down_14 <- diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Down',]$Gene
In [178]:
a <- list(up_0 = up_0,
          down_0 = down_0,
           up_14 = up_14,
           down_14 = down_14)
p1=ggvenn(a, c("up_0", "down_0","up_14",'down_14')) 
p1
No description has been provided for this image

t7_14¶

In [514]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t7_t14_cyc'
read_dir <- file.path(read_dir,run_name)
In [515]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
BIN1 0.0033546050.0003882261 0.000515350110FALSEFALSEFALSEFALSE
CKS1B0.0027099940.0005753519 0.000454056910FALSEFALSEFALSEFALSE
PRDX10.0031960590.0005124655 0.000408712810FALSEFALSE TRUE TRUE
KRT8 0.0038899180.0002229171 0.000548130810FALSEFALSEFALSEFALSE
SRSF30.0028382130.0003422896 0.000434381110FALSEFALSEFALSEFALSE
TPM1 0.0023733090.0003037851-0.000529684510FALSEFALSEFALSEFALSE
In [516]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [517]:
drivers[drivers$is_in_Pathway,]
A data.table: 4 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
PRDX1 0.0031960590.00051246550.000408712810FALSEFALSE TRUETRUE1322
H2AFZ 0.0040756580.00058476430.000647578710FALSE TRUEFALSETRUE 8 8
ALDH3A10.0044520390.00054620900.000762479310FALSE TRUEFALSETRUE 6 5
NQO1 0.0020750330.00021670710.0003687808 9FALSEFALSE TRUETRUE2324
In [495]:
fread(file.path(read_dir,'all_driver_types_match_counts.csv'))
A data.table: 10 × 7
seedshap_totalshap_0shap_1grad_totalgrad_0grad_1
<int><int><int><int><int><int><int>
027272623 522
1252525252125
2262625242319
3272827252425
4272628262225
5282827242425
6272728262725
7262626262124
8272625242422
9262626242317
In [497]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [498]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
    file_name = paste0('driver_info_',i,'.csv')
    tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
    df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
    
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [499]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [500]:
pathway_hallmark = openxlsx::read.xlsx(file.path('./resources/pathway', 'Gene_signature_list_paper_supp.xlsx'))
In [501]:
plot_df$pw = plot_df$Gene %in% pathway_hallmark$HALLMARK_FATTY_ACID_METABOLISM
In [137]:
ggplot(data = plot_df)+
geom_boxplot(aes(x=variable,y=value,fill=pw))+
theme_classic()
Warning message:
“Removed 398 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
No description has been provided for this image
In [502]:
gl <- drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:10]
In [503]:
tmp <- plot_df[plot_df$Gene %in% gl,]

tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [504]:
gl
  1. 'GSTM3'
  2. 'UBB'
  3. 'ITGA2'
  4. 'PKM'
  5. 'MYL6'
  6. 'ALDH3A1'
  7. 'ENO1'
  8. 'H2AFZ'
  9. 'HSPA1A'
  10. 'KRT8'
In [141]:
ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
  ggridges::geom_density_ridges() +
  ggridges::theme_ridges() + 
  theme(legend.position = "none")
Picking joint bandwidth of 0.000269

No description has been provided for this image
In [518]:
drivers$norm_shap <-  normalize(drivers$weight_shap_total_mean)
ddf <- drivers[order(drivers$weight_shap_total_mean,decreasing = T),][1:10,] %>% as.data.frame()
ddf$V1 <- factor(ddf$V1,levels = rev(ddf$V1))
In [519]:
bar_p <-  ggplot(ddf) +
  geom_bar(aes(x = V1, y = norm_shap,fill=norm_shap),stat = "identity",width = 0.6, alpha=1)+
    scale_fill_gradient(low = "#659999",high = "#f4791f")+
    coord_flip() +
    theme_bw()+
    ylab('Normalized SHAP weight')+
    xlab('Driver genes')+
    ggtitle("Modeling between T7 T14")+
    #scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
    theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5,hjust = 0.5),
        axis.title.y = element_text(vjust = 5,hjust = 0.5),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
       axis.text.x = element_text(vjust = 0.5),
       axis.text.y = element_text(colour = c(rep('black',times = 2),'#BA002B','black','#BA002B',rep('black',times = 5))),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
        panel.border = element_rect(size = 1.5),
        panel.grid = element_blank(),
        panel.grid.minor.x = element_line(linetype = 5),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(10,'points'),
       legend.position = 'none'

    )
bar_p
Warning message:
“Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.”
No description has been provided for this image
In [509]:
ggsave(plot = bar_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_bar_p.pdf',
       width =12/1.5, height =16/1.5)
ggsave(plot = bar_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_bar_p.png',
       width =12/1.5, height =16/1.5)
In [520]:
drivers_exp <- fread(file.path(read_dir,'driver_summary_shap_total_addexp.csv'))
In [521]:
drivers_exp_long<-drivers_exp %>%as.data.frame %>% 
    mutate(direction=ifelse(weight_grad_total_dir_mean > 0,'pos','neg')) %>%
    mutate(rank=rank(-drivers_exp$weight_shap_total_mean)) %>%
    mutate(top20 = ifelse(rank<=20,TRUE,FALSE)) %>%
    mutate(top10 = ifelse(rank<=10,TRUE,FALSE)) %>%
    dplyr::select(c('V1','time0_exp','time3_exp','time7_exp','time14_exp','direction','top20','top10')) %>%
    reshape2::melt(id.vars = c("V1",'direction','top20','top10'), #需保留的不参与聚合的变量列名
                  measure.vars = c('time0_exp','time3_exp','time7_exp','time14_exp'),#需要聚合的变量s1-s10
                  variable.name = c('time_point'),#聚合变量的新列名
                  value.name = 'exp')#聚合值的新列名
In [522]:
head(drivers_exp_long)
A data.frame: 6 × 6
V1directiontop20top10time_pointexp
<chr><chr><lgl><lgl><fct><dbl>
1GSTM3 posTRUETRUEtime0_exp-0.4995057
2UBB posTRUETRUEtime0_exp-0.4454428
3ITGA2 negTRUETRUEtime0_exp 0.1798690
4PKM posTRUETRUEtime0_exp-0.1132269
5MYL6 posTRUETRUEtime0_exp-1.2952663
6ALDH3A1posTRUETRUEtime0_exp-0.5168157
In [523]:
pos_drivers <- drivers_exp_long %>% filter(direction=='pos')
neg_drivers <- drivers_exp_long %>% filter(direction=='neg')
In [550]:
pos_time_p<-ggplot(pos_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
  geom_xspline(spline_shape = -0.5)+
  geom_xspline(data = pos_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
  ggrepel::geom_text_repel(data=pos_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
                  aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
  geom_point(size=3)+
  geom_point(data = pos_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
  xlab('Time points (Days)')+
  ylab('Normalized expression')+
  ggtitle('Time-course gene expression')+
  theme_bw()+
  scale_color_manual(values = c('TRUE'= '#259CA2BB','FALSE'='#00000033'))+
  scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
  #ylim(-1.5,1.5)+
  scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.6,1.6))+
  theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5),
        axis.title.y = element_text(vjust = 6),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
       axis.text.x = element_text(vjust = -2),
        axis.text.y = element_text(hjust = -1.5),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(-10,'points'),
       axis.line = element_line(linewidth = 1),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
       panel.border = element_rect(linewidth = 1.5),
       panel.grid = element_blank(),
       #panel.grid.major.x =element_line(),
       legend.position = 'None')


pos_time_p
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_x_spline()`).”
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).”
No description has been provided for this image
In [551]:
ggsave(plot = pos_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_pos_driver_cross_time_ggalt.pdf',
       width =16/1.5, height =9/1.5)
ggsave(plot = pos_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_pos_driver_cross_time_ggalt.png',
       width =16/1.5, height =9/1.5)
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_x_spline()`).”
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).”
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_x_spline()`).”
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).”
In [552]:
neg_time_p<-ggplot(neg_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
  geom_xspline(spline_shape = -0.5)+
  geom_xspline(data = neg_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
  ggrepel::geom_text_repel(data=neg_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
                  aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
  geom_point(size=3)+
  geom_point(data = neg_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
  xlab('Time points (Days)')+
  ylab('Normalized expression')+
  ggtitle('Time-course gene expression')+
  theme_bw()+
  scale_color_manual(values = c('TRUE'= '#E0A965BB','FALSE'='#00000033'))+
  scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
  scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.7,1.7))+
  theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5),
        axis.title.y = element_text(vjust = 6),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
       axis.text.x = element_text(vjust = -2),
        axis.text.y = element_text(hjust = -1.5),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(-10,'points'),
       axis.line = element_line(linewidth = 1),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
       panel.border = element_rect(linewidth = 1.5),
       panel.grid = element_blank(),
       #panel.grid.major.x =element_line(),
       legend.position = 'None')


neg_time_p
No description has been provided for this image
In [553]:
ggsave(plot = neg_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_neg_driver_cross_time_ggalt.pdf',
       width =16/1.5, height =9/1.5)
ggsave(plot = neg_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_neg_driver_cross_time_ggalt.png',
       width =16/1.5, height =9/1.5)
In [536]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
'select()' returned 1:1 mapping between keys and columns

Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“1.67% of input gene IDs are fail to map...”
In [541]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>% 
  dplyr::select(gs_name, entrez_gene)

m_t2g$gs_name <- m_t2g$gs_name %>% 
    stringr::str_replace_all(pattern = 'HALLMARK_',replacement = '') %>%
    stringr::str_replace_all(pattern = '_',replacement = ' ') %>%
    stringr::str_to_title()

kk <- enrichKEGG(gene         = names(gs),
                 #keyType      = 'uniprot',
                 organism     = 'hsa',
                 pvalueCutoff = 0.05)
#head(kk)
em <- enricher(names(gs), 
           TERM2GENE=m_t2g,
          minGSSize    = 0,
              #maxGSSize    = 500,
              pvalueCutoff = 1,
              #scoreType = "pos"
              qvalueCutoff = 1,
              #eps = eps,
              #verbose      = FALSE
              )
In [542]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()

for (i in 1:nrow(em@result)){
    if (i==1){
        
        ls <- em@geneSets[em@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
        
    }else{
        
    ls <- em@geneSets[em@result[i,'ID']]
    where <- which(high_df$ENTREZID %in% ls[[1]])
    tmp <- high_df[where,]
    tmp$gs <- gs_name[i]
    tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
    high_plot_df <- rbind(high_plot_df,tmp)
    high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
        
    }
}

high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [543]:
pRidge <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = '   ') +
  scale_fill_gradientn(name = "Median",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.text.y = element_text(size = 10,color="black"),
        axis.text.x = element_text(size = 12,color="black"),
       panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank() # get rid of minor grid
       )

pRidge
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.403

No description has been provided for this image
In [544]:
#ggplot2 修改图例的一些操作 https://zhuanlan.zhihu.com/p/166529941
pRidge_H <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = 'Hallmark',fill = 'Log10 mean of weight') +
    xlab('SHAP weight')+
    ylab('Gene set')+
  scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
  scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18), 
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
       )

pRidge_H
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.403

No description has been provided for this image
In [545]:
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_pRidge_H.pdf',
       width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_pRidge_H.png',
       width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.403

Picking joint bandwidth of 0.403

In [546]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- kk@result[1:30,]$Description
high_mean_ls <-c()

for (i in 1:nrow(kk@result[1:30,])){
    if (i==1){
        
        ls <- kk@geneSets[kk@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
        
    }else{
        
    ls <- kk@geneSets[kk@result[i,'ID']]
    where <- which(high_df$ENTREZID %in% ls[[1]])
    tmp <- high_df[where,]
    tmp$gs <- gs_name[i]
    tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
    high_plot_df <- rbind(high_plot_df,tmp)
    high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
        
    }
}

high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [547]:
pRidge_K <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = 'KEGG',fill = 'Log10 mean of weight') +
    xlab('SHAP weight')+
    ylab('Gene set')+
  scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
  scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = 'white'), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18), 
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
       )

pRidge_K
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.265

No description has been provided for this image
In [548]:
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_pRidge_K.pdf',
       width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_pRidge_K.png',
       width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.265

Picking joint bandwidth of 0.265

In [8]:
chord_data <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_t7_orgin_state_transition_adata_meta.csv')
In [9]:
chord_data <- chord_data %>% select(c('condition','metacell_label2'))
In [10]:
# Transform input data in a adjacency matrix
adjacencyData <- with(chord_data, table(condition, metacell_label2))
In [11]:
adjacencyData
             metacell_label2
condition     0 in 0 0 in 0-7 0 in 1 1 in 0 1 in 0-7 1 in 1 2 in 0 2 in 0-7
  t0 cycling     537        0      0    110        0      0    106        0
  t14 cycling      0        0    762      0        0    346      0        0
  t7 cycling       0      281      0      0      223      0      0      191
             metacell_label2
condition     2 in 1 3 in 0 3 in 0-7 3 in 1 4 in 0 4 in 0-7 4 in 1 5 in 1
  t0 cycling       0     88        0      0     37        0      0      0
  t14 cycling    237      0        0    176      0        0    159    133
  t7 cycling       0      0       87      0      0       36      0      0
             metacell_label2
condition     6 in 1 7 in 1 8 in 1
  t0 cycling       0      0      0
  t14 cycling    120     51      9
  t7 cycling       0      0      0
In [49]:
dim(adjacencyData)
  1. 3
  2. 19
In [53]:
#https://www.jianshu.com/p/1ebeaaed6f4e
library(RColorBrewer)
display.brewer.all() #显示所有调色板
No description has been provided for this image
In [56]:
grid.col <-c( brewer.pal(8, "Set2"), brewer.pal(8, "Accent"),brewer.pal(6, "Set3"))
grid.col
  1. '#66C2A5'
  2. '#FC8D62'
  3. '#8DA0CB'
  4. '#E78AC3'
  5. '#A6D854'
  6. '#FFD92F'
  7. '#E5C494'
  8. '#B3B3B3'
  9. '#7FC97F'
  10. '#BEAED4'
  11. '#FDC086'
  12. '#FFFF99'
  13. '#386CB0'
  14. '#F0027F'
  15. '#BF5B17'
  16. '#666666'
  17. '#8DD3C7'
  18. '#FFFFB3'
  19. '#BEBADA'
  20. '#FB8072'
  21. '#80B1D3'
  22. '#FDB462'
In [ ]:

In [57]:
#https://www.jianshu.com/p/73c246b87d82
chordDiagram(
  adjacencyData,  grid.col = grid.col, 
  annotationTrack = c("grid"), 
  preAllocateTracks = list(
    track.height = max(strwidth(unlist(dimnames(adjacencyData))))
  )
)
circos.track(
  track.index = 1, panel.fun = function(x, y) {
    circos.text(
      CELL_META$xcenter, CELL_META$ylim[1], 
      CELL_META$sector.index,  facing = "clockwise", 
      niceFacing = TRUE, adj = c(0, 0.5)
    )
  }, bg.border = NA
)
No description has been provided for this image
In [61]:
pdf('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_t7_orgin_state_transition_adata_meta.pdf')
chordDiagram(
  adjacencyData,  grid.col = grid.col, 
  annotationTrack = c("grid"), 
  preAllocateTracks = list(
    track.height = max(strwidth(unlist(dimnames(adjacencyData))))
  )
)
circos.track(
  track.index = 1, panel.fun = function(x, y) {
    circos.text(
      CELL_META$xcenter, CELL_META$ylim[1], 
      CELL_META$sector.index,  facing = "clockwise", 
      niceFacing = TRUE, adj = c(0, 0.5)
    )
  }, bg.border = NA
)
dev.off()
png: 2

t14 cyc_ncyc¶

In [804]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t14_ncyc_cyc'
read_dir <- file.path(read_dir,run_name)
In [795]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
HLA-B 0.0066118370.0014072035-0.00388835810FALSEFALSEFALSEFALSE
H2AFZ 0.0015434790.0003817574 0.00159619110FALSE TRUEFALSE TRUE
UBC 0.0030148710.0006034041-0.00355204510FALSEFALSEFALSEFALSE
IGFBP50.0117281110.0037999116 0.01300407910FALSEFALSEFALSEFALSE
GSTM3 0.0092013450.0016104131 0.00469491910FALSEFALSEFALSEFALSE
SAT1 0.0038243560.0007941552-0.00198726110FALSEFALSEFALSEFALSE
In [796]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [797]:
drivers[drivers$is_in_Pathway,]
A data.table: 5 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
H2AFZ 0.00154347910.0003817574 0.001596190810FALSE TRUEFALSETRUE2220
JUNB 0.00168353710.0008096141-0.0029814138 7 TRUEFALSE TRUETRUE20 9
SOD2 0.00022155760.0000000000 0.0007589676 1FALSEFALSE TRUETRUE6339
LGALS10.00013876970.0000000000 0.0001830989 1FALSE TRUEFALSETRUE7265
PFKP 0.00024212560.0000000000 0.0002091714 1FALSEFALSE TRUETRUE6163
In [798]:
drivers[order(weight_shap_total_mean,decreasing = T),]
A data.table: 81 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
IGFBP5 0.01172811090.0037999116 0.013004078710FALSEFALSEFALSEFALSE 1 1
GSTM3 0.00920134460.0016104131 0.004694918910FALSEFALSEFALSEFALSE 2 4
HLA-B 0.00661183690.0014072035-0.003888358010FALSEFALSEFALSEFALSE 3 5
CD24 0.00443675980.0006784051 0.005839140210FALSEFALSEFALSEFALSE 4 2
HIST1H4C0.00406893460.0005085617-0.005728028610FALSEFALSEFALSEFALSE 5 3
SAT1 0.00382435640.0007941552-0.001987260610FALSEFALSEFALSEFALSE 616
ELF3 0.00317622010.0007735006-0.0031860740 9 TRUEFALSEFALSEFALSE 7 7
UCHL1 0.00310854630.0010805764 0.001690394810FALSEFALSEFALSEFALSE 819
UBC 0.00301487110.0006034041-0.003552045410FALSEFALSEFALSEFALSE 9 6
AKR1C3 0.00300993620.0013239239 0.001103967910FALSEFALSEFALSEFALSE1030
UBE2C 0.00256996770.0006142277-0.0025862740 9FALSEFALSEFALSEFALSE1112
HLA-C 0.00256604230.0005627148-0.0007788526 9FALSEFALSEFALSEFALSE1238
IL32 0.00221632230.0002488609-0.0001668038 5FALSEFALSEFALSEFALSE1366
CCND1 0.00210712440.0003571276-0.002019259810FALSEFALSEFALSEFALSE1414
HLA-A 0.00206876480.0004496342-0.0028002224 7FALSEFALSEFALSEFALSE1510
MT2A 0.00204673660.0004843694-0.0020090360 9FALSEFALSEFALSEFALSE1615
EIF5A 0.00187837510.0004235593 0.0013126781 9FALSEFALSEFALSEFALSE1725
SERPINH10.00175999360.0005120919 0.0027856450 8FALSEFALSEFALSEFALSE1811
CSTB 0.00170334080.0003514902 0.0025668851 9FALSEFALSEFALSEFALSE1913
JUNB 0.00168353710.0008096141-0.0029814138 7 TRUEFALSE TRUE TRUE20 9
LDHB 0.00157449760.0009098976 0.0003617867 8FALSEFALSEFALSEFALSE2156
H2AFZ 0.00154347910.0003817574 0.001596190810FALSE TRUEFALSE TRUE2220
CTSD 0.00142677330.0002987906 0.0013005452 6FALSEFALSEFALSEFALSE2326
PTTG1 0.00136443070.0005340962 0.0008319057 5FALSEFALSEFALSEFALSE2437
H2AFX 0.00132873160.0002748743-0.0019340660 8FALSEFALSEFALSEFALSE2517
KLF4 0.00130444420.0005156657-0.0031121433 8 TRUEFALSEFALSEFALSE26 8
RRBP1 0.00119250600.0002110271-0.0006431049 5FALSEFALSEFALSEFALSE2744
MYBL2 0.00114839090.0005146034 0.0005562596 7 TRUEFALSEFALSEFALSE2845
ENO1 0.00108159230.0003481806 0.0011467627 7FALSEFALSEFALSEFALSE2929
PGK1 0.00095100340.0002593502 0.0015275956 5FALSEFALSEFALSEFALSE3021
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
CCNB1 3.756681e-047.193587e-05-5.032483e-043FALSEFALSEFALSEFALSE5247
HMGA1 3.704270e-041.749644e-05 1.017245e-032 TRUEFALSEFALSEFALSE5332
TPM1 3.272433e-041.606393e-04-4.370322e-042FALSEFALSEFALSEFALSE5451
JUND 3.153070e-043.122225e-04 8.608124e-043 TRUEFALSEFALSEFALSE5535
CLTB 3.133257e-040.000000e+00 3.675563e-051FALSEFALSEFALSEFALSE5674
PLK2 2.963634e-040.000000e+00 1.643981e-041FALSEFALSEFALSEFALSE5767
ASF1B 2.921398e-041.729619e-05 4.507829e-042FALSEFALSEFALSEFALSE5849
PCNA 2.809295e-041.516539e-04 6.670484e-042FALSEFALSEFALSEFALSE5942
BIN1 2.658711e-040.000000e+00 1.353187e-051FALSEFALSEFALSEFALSE6079
PFKP 2.421256e-040.000000e+00 2.091714e-041FALSEFALSE TRUE TRUE6163
CEBPD 2.357836e-045.450518e-05-7.560967e-042 TRUEFALSEFALSEFALSE6240
SOD2 2.215576e-040.000000e+00 7.589676e-041FALSEFALSE TRUE TRUE6339
HSPA5 2.087326e-040.000000e+00-4.155351e-041FALSEFALSEFALSEFALSE6453
TUBA1B1.948145e-040.000000e+00-7.872681e-051FALSEFALSEFALSEFALSE6571
CEBPB 1.856055e-041.775317e-04 6.901383e-052 TRUEFALSEFALSEFALSE6672
PPIF 1.778167e-040.000000e+00 9.578473e-061FALSEFALSEFALSEFALSE6780
BRCA1 1.742907e-041.199398e-04-3.808969e-042FALSEFALSEFALSEFALSE6855
SRSF3 1.632443e-040.000000e+00 1.503613e-041FALSEFALSEFALSEFALSE6968
NOP56 1.548300e-040.000000e+00-2.461439e-041FALSEFALSEFALSEFALSE7061
MYL6 1.541809e-040.000000e+00-5.557641e-041FALSEFALSEFALSEFALSE7146
LGALS11.387697e-040.000000e+00 1.830989e-041FALSE TRUEFALSE TRUE7265
PHGDH 1.338904e-040.000000e+00 1.095444e-061FALSEFALSEFALSEFALSE7381
AURKA 1.316213e-040.000000e+00-3.628317e-051FALSEFALSEFALSEFALSE7475
TUBB4B1.309983e-040.000000e+00 3.096596e-041FALSEFALSEFALSEFALSE7560
STAT1 1.154839e-040.000000e+00 6.277068e-051 TRUEFALSEFALSEFALSE7673
IRF1 1.098877e-040.000000e+00-4.552711e-041 TRUEFALSEFALSEFALSE7748
GATA2 1.039767e-040.000000e+00-4.251870e-041 TRUEFALSEFALSEFALSE7852
FOS 9.773594e-050.000000e+00-3.522839e-041 TRUEFALSEFALSEFALSE7959
CDK1 9.369986e-050.000000e+00-3.939987e-041FALSEFALSEFALSEFALSE8054
FOXM1 9.080476e-050.000000e+00 1.835079e-041 TRUEFALSEFALSEFALSE8164
In [805]:
read_dir
'/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new//main_PC9_LUNG_run10_t14_ncyc_cyc'
In [806]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [807]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
    file_name = paste0('driver_info_',i,'.csv')
    tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
    df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
    
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [808]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [809]:
plot_df
A data.frame: 1380 × 3
Genevariablevalue
<chr><chr><dbl>
ACTG2 run00.0003696884
ACTN1 run0 NA
AKR1C3run00.0028334604
ANXA2 run00.0012547938
APP run00.0012565161
ARF4 run0 NA
ASF1B run00.0003938598
ATF3 run00.0004590547
ATF4 run0 NA
AURKA run00.0013162135
AURKB run00.0006685557
BARD1 run0 NA
BIN1 run0 NA
BIRC5 run0 NA
BRCA1 run00.0007866431
CALD1 run0 NA
CALR run00.0012828062
CCNA2 run0 NA
CCNB1 run00.0004704217
CCND1 run00.0016369422
CD24 run00.0038024938
CD44 run00.0012120469
CD59 run00.0026735116
CDC20 run0 NA
CDCA3 run0 NA
CDK1 run00.0005936714
CDKN3 run0 NA
CEBPB run00.0008024935
CEBPD run00.0004966382
CENPA run0 NA
⋮⋮⋮
PLK1 run90.0002112278
PLK2 run9 NA
PPIF run9 NA
PRDX1 run90.0004295610
PSMB1 run9 NA
PSMC4 run9 NA
PTTG1 run90.0022282538
RFC4 run9 NA
RRBP1 run9 NA
RRM2 run9 NA
SAT1 run90.0052132864
SERPINH1run90.0020251341
SOD2 run9 NA
SPC25 run9 NA
SRSF3 run9 NA
STAT1 run90.0011548388
TCOF1 run90.0002267744
TOP2A run9 NA
TPM1 run90.0015226273
TPX2 run9 NA
TRIP13 run9 NA
TUBA1B run90.0019481454
TUBA4A run90.0006985455
TUBB4B run90.0003493046
TYMS run90.0007126650
UBB run90.0003479973
UBC run90.0029201839
UBE2C run90.0019457745
UCHL1 run90.0039379844
ZWINT run9 NA
In [810]:
gl <- drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:10]
In [811]:
tmp <- plot_df[plot_df$Gene %in% gl,]

tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [812]:
gl
  1. 'IGFBP5'
  2. 'GSTM3'
  3. 'HLA-B'
  4. 'CD24'
  5. 'HIST1H4C'
  6. 'SAT1'
  7. 'ELF3'
  8. 'UCHL1'
  9. 'UBC'
  10. 'AKR1C3'
In [816]:
ridge_p <- ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
  ggridges::geom_density_ridges() +
  ggridges::theme_ridges() + 
  ggsci::scale_fill_npg()+
xlab('SHAP weight')+
ylab('Driver genes')+
ggtitle("Weight through model with different seeds")+
scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   legend.position = 'none',
   panel.grid = element_blank()
      
)
ridge_p
Picking joint bandwidth of 0.000677

Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
No description has been provided for this image
In [817]:
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_ridge_p.pdf',
       width =16/1.5, height =16/1.5)
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_ridge_p.png',
       width =16/1.5, height =16/1.5)
Picking joint bandwidth of 0.000677

Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
Picking joint bandwidth of 0.000677

Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [822]:
drivers_exp <- fread(file.path(read_dir,'driver_summary_shap_total_addexp.csv'))
In [823]:
head(drivers_exp)
A data.table: 6 × 13
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwaytime0_exptime3_exptime7_exptime14_exp
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl><dbl><dbl>
IGFBP5 0.0117281110.0037999116 0.01300407910FALSEFALSEFALSEFALSE-0.4614117-0.0006751149-0.24896230 0.19265650
GSTM3 0.0092013450.0016104131 0.00469491910FALSEFALSEFALSEFALSE-0.4995057-0.4245171000-0.55196540 0.52083343
HLA-B 0.0066118370.0014072035-0.00388835810FALSEFALSEFALSEFALSE-1.3243920-0.1092498800-0.01360234 0.39492068
CD24 0.0044367600.0006784051 0.00583914010FALSEFALSEFALSEFALSE-1.4788445 0.0453659200 0.24021030 0.27349910
HIST1H4C0.0040689350.0005085617-0.00572802910FALSEFALSEFALSEFALSE 1.5583935-0.4402804400-0.44383672-0.02076823
SAT1 0.0038243560.0007941552-0.00198726110FALSEFALSEFALSEFALSE-1.0865847 0.3337592500 0.26571733 0.01365761
In [824]:
drivers_exp_long<-drivers_exp %>%as.data.frame %>% 
    mutate(direction=ifelse(weight_grad_total_dir_mean > 0,'pos','neg')) %>%
    mutate(rank=rank(-drivers_exp$weight_shap_total_mean)) %>%
    mutate(top20 = ifelse(rank<=20,TRUE,FALSE)) %>%
    mutate(top10 = ifelse(rank<=10,TRUE,FALSE)) %>%
    dplyr::select(c('V1','time0_exp','time3_exp','time7_exp','time14_exp','direction','top20','top10')) %>%
    reshape2::melt(id.vars = c("V1",'direction','top20','top10'), #需保留的不参与聚合的变量列名
                  measure.vars = c('time0_exp','time3_exp','time7_exp','time14_exp'),#需要聚合的变量s1-s10
                  variable.name = c('time_point'),#聚合变量的新列名
                  value.name = 'exp')#聚合值的新列名
In [825]:
head(drivers_exp_long)
A data.frame: 6 × 6
V1directiontop20top10time_pointexp
<chr><chr><lgl><lgl><fct><dbl>
1IGFBP5 posTRUETRUEtime0_exp-0.4614117
2GSTM3 posTRUETRUEtime0_exp-0.4995057
3HLA-B negTRUETRUEtime0_exp-1.3243920
4CD24 posTRUETRUEtime0_exp-1.4788445
5HIST1H4CnegTRUETRUEtime0_exp 1.5583935
6SAT1 negTRUETRUEtime0_exp-1.0865847
In [826]:
pos_drivers <- drivers_exp_long %>% filter(direction=='pos')
neg_drivers <- drivers_exp_long %>% filter(direction=='neg')
In [827]:
pos_time_p<-ggplot(pos_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
  geom_xspline(spline_shape = -0.5)+
  geom_xspline(data = pos_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
  ggrepel::geom_text_repel(data=pos_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
                  aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
  geom_point(size=3)+
  geom_point(data = pos_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
  xlab('Time points (Days)')+
  ylab('Normalized expression')+
  ggtitle('Time-course gene expression')+
  theme_bw()+
  scale_color_manual(values = c('TRUE'= '#259CA2BB','FALSE'='#00000033'))+
  scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
  #ylim(-1.5,1.5)+
  scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.6,1.6))+
  theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5),
        axis.title.y = element_text(vjust = 6),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
       axis.text.x = element_text(vjust = -2),
        axis.text.y = element_text(hjust = -1.5),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(-10,'points'),
       axis.line = element_line(linewidth = 1),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
       panel.border = element_rect(linewidth = 1.5),
       panel.grid = element_blank(),
       #panel.grid.major.x =element_line(),
       legend.position = 'None')


pos_time_p
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_x_spline()`).”
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).”
No description has been provided for this image
In [551]:
ggsave(plot = pos_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_pos_driver_cross_time_ggalt.pdf',
       width =16/1.5, height =9/1.5)
ggsave(plot = pos_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_pos_driver_cross_time_ggalt.png',
       width =16/1.5, height =9/1.5)
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_x_spline()`).”
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).”
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_x_spline()`).”
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).”
In [828]:
neg_time_p<-ggplot(neg_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
  geom_xspline(spline_shape = -0.5)+
  geom_xspline(data = neg_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
  ggrepel::geom_text_repel(data=neg_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
                  aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
  geom_point(size=3)+
  geom_point(data = neg_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
  xlab('Time points (Days)')+
  ylab('Normalized expression')+
  ggtitle('Time-course gene expression')+
  theme_bw()+
  scale_color_manual(values = c('TRUE'= '#E0A965BB','FALSE'='#00000033'))+
  scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
  scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.7,1.7))+
  theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5),
        axis.title.y = element_text(vjust = 6),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
       axis.text.x = element_text(vjust = -2),
        axis.text.y = element_text(hjust = -1.5),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(-10,'points'),
       axis.line = element_line(linewidth = 1),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
       panel.border = element_rect(linewidth = 1.5),
       panel.grid = element_blank(),
       #panel.grid.major.x =element_line(),
       legend.position = 'None')


neg_time_p
No description has been provided for this image
In [553]:
ggsave(plot = neg_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_neg_driver_cross_time_ggalt.pdf',
       width =16/1.5, height =9/1.5)
ggsave(plot = neg_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_neg_driver_cross_time_ggalt.png',
       width =16/1.5, height =9/1.5)
In [ ]:

In [189]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
'select()' returned 1:1 mapping between keys and columns

Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“4.94% of input gene IDs are fail to map...”
In [190]:
kk <- enrichKEGG(gene         = gene$ENTREZID,
                 #keyType      = 'uniprot',
                 organism     = 'hsa',
                 pvalueCutoff = 0.1)
head(kk)
Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...

Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...

A data.frame: 6 × 11
categorysubcategoryIDDescriptionGeneRatioBgRatiopvaluep.adjustqvaluegeneIDCount
<chr><chr><chr><chr><chr><chr><dbl><dbl><dbl><chr><int>
hsa05167Human Diseases Infectious disease: viralhsa05167Kaposi sarcoma-associated herpesvirus infection10/64194/87641.200851e-060.00016825810.00011986493106/7316/595/3107/3105/4609/3091/3725/2353/677210
hsa04218Cellular ProcessesCell growth and death hsa04218Cellular senescence 9/64 156/87641.699576e-060.00016825810.00011986493106/595/3107/3105/4605/4609/891/2305/983 9
hsa05166Human Diseases Infectious disease: viralhsa05166Human T-cell leukemia virus 1 infection 9/64 222/87642.995343e-050.00197692630.00140833673106/595/3107/3105/811/9232/4609/3725/2353 9
hsa05169Human Diseases Infectious disease: viralhsa05169Epstein-Barr virus infection 8/64 202/87641.014199e-040.00327446100.00233268363106/595/3107/3105/811/4609/3725/6772 8
hsa05203Human Diseases Cancer: overview hsa05203Viral carcinogenesis 8/64 204/87641.086389e-040.00327446100.00233268363106/595/3107/3105/5315/3725/2959/983 8
hsa00010Metabolism Carbohydrate metabolism hsa00010Glycolysis / Gluconeogenesis 5/64 67/8764 1.207803e-040.00327446100.00233268363945/2023/5315/5230/5214 5
In [829]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
'select()' returned 1:1 mapping between keys and columns

Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“4.94% of input gene IDs are fail to map...”
In [830]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>% 
  dplyr::select(gs_name, entrez_gene)

m_t2g$gs_name <- m_t2g$gs_name %>% 
    stringr::str_replace_all(pattern = 'HALLMARK_',replacement = '') %>%
    stringr::str_replace_all(pattern = '_',replacement = ' ') %>%
    stringr::str_to_title()

kk <- enrichKEGG(gene         = names(gs),
                 #keyType      = 'uniprot',
                 organism     = 'hsa',
                 pvalueCutoff = 0.05)
#head(kk)
em <- enricher(names(gs), 
           TERM2GENE=m_t2g,
          minGSSize    = 0,
              #maxGSSize    = 500,
              pvalueCutoff = 1,
              #scoreType = "pos"
              qvalueCutoff = 1,
              #eps = eps,
              #verbose      = FALSE
              )
In [831]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()

for (i in 1:nrow(em@result)){
    if (i==1){
        
        ls <- em@geneSets[em@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
        
    }else{
        
    ls <- em@geneSets[em@result[i,'ID']]
    where <- which(high_df$ENTREZID %in% ls[[1]])
    tmp <- high_df[where,]
    tmp$gs <- gs_name[i]
    tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
    high_plot_df <- rbind(high_plot_df,tmp)
    high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
        
    }
}

high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [832]:
#ggplot2 修改图例的一些操作 https://zhuanlan.zhihu.com/p/166529941
pRidge_H <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = 'Hallmark',fill = 'Log10 mean of weight') +
    xlab('SHAP weight')+
    ylab('Gene set')+
  scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
  scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18), 
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
       )

pRidge_H
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.322

No description has been provided for this image
In [833]:
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_pRidge_H.pdf',
       width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_pRidge_H.png',
       width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.322

Picking joint bandwidth of 0.322

In [834]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- kk@result[1:30,]$Description
high_mean_ls <-c()

for (i in 1:nrow(kk@result[1:30,])){
    if (i==1){
        
        ls <- kk@geneSets[kk@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
        
    }else{
        
    ls <- kk@geneSets[kk@result[i,'ID']]
    where <- which(high_df$ENTREZID %in% ls[[1]])
    tmp <- high_df[where,]
    tmp$gs <- gs_name[i]
    tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
    high_plot_df <- rbind(high_plot_df,tmp)
    high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
        
    }
}

high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [835]:
pRidge_K <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = 'KEGG',fill = 'Log10 mean of weight') +
    xlab('SHAP weight')+
    ylab('Gene set')+
  scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
  scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = 'white'), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18), 
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
       )

pRidge_K
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.276

No description has been provided for this image
In [836]:
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_pRidge_K.pdf',
       width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_pRidge_K.png',
       width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.276

Picking joint bandwidth of 0.276

t0_cyc_ncyc¶

In [ ]:

In [837]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_ncyc_cyc'
read_dir <- file.path(read_dir,run_name)
In [838]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
PSMC40.00092378560.0003805723 0.00399682310FALSEFALSEFALSEFALSE
LMO7 0.00085112440.0001933136 0.01870590410FALSEFALSEFALSEFALSE
TGM2 0.00080142640.0002573693 0.00267940110FALSEFALSEFALSEFALSE
CD24 0.00133817590.0002601511 0.00629678810FALSEFALSEFALSEFALSE
TPM1 0.00180603970.0003299156 0.00861467610FALSEFALSEFALSEFALSE
SRSF70.00125054690.0003662743-0.00367301110FALSEFALSEFALSEFALSE
In [839]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [840]:
drivers[drivers$is_in_Pathway,]
A data.table: 0 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
In [841]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [842]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
    file_name = paste0('driver_info_',i,'.csv')
    tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
    df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
    
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [843]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [844]:
plot_df
A data.frame: 340 × 3
Genevariablevalue
<chr><chr><dbl>
BARD1 run06.258099e-04
CD24 run01.343648e-03
CD9 run0 NA
CEBPD run01.352879e-03
DAAM1 run01.452751e-03
GTF2B run0 NA
HSPA2 run01.362294e-04
KLF10 run09.780429e-04
KLF5 run08.033533e-04
LAMP2 run0 NA
LMO7 run08.168148e-04
MAFF run01.181879e-03
MSH6 run07.886995e-04
MYLK run02.815358e-05
MYO1B run04.807452e-04
PLK2 run05.894743e-04
PRDM1 run01.185135e-04
PSMC4 run01.325408e-03
PTPRK run0 NA
RAB31 run01.050034e-03
RRBP1 run02.118842e-03
SLC1A5run02.550872e-03
SRSF7 run09.063769e-04
TFAP2Crun01.276508e-04
TFDP1 run01.033369e-03
TGM2 run01.200146e-03
TPM1 run02.229035e-03
TRIB1 run0 NA
TRIB3 run02.341409e-03
TUBA1Brun01.615746e-03
⋮⋮⋮
DAAM1 run91.519394e-03
GTF2B run9 NA
HSPA2 run96.149456e-05
KLF10 run98.660538e-04
KLF5 run91.084358e-03
LAMP2 run98.435934e-04
LMO7 run91.018867e-03
MAFF run93.525586e-04
MSH6 run99.504799e-04
MYLK run91.742803e-05
MYO1B run96.943114e-04
PLK2 run95.458337e-04
PRDM1 run91.289089e-04
PSMC4 run96.307576e-04
PTPRK run9 NA
RAB31 run9 NA
RRBP1 run92.204840e-03
SLC1A5run92.257197e-03
SRSF7 run91.257358e-03
TFAP2Crun93.653187e-04
TFDP1 run95.367805e-04
TGM2 run96.890538e-04
TPM1 run91.446126e-03
TRIB1 run91.183589e-03
TRIB3 run91.785804e-03
TUBA1Brun96.444304e-04
TUBA4Arun91.007705e-03
TUBB4Brun93.454001e-03
ULK1 run91.139592e-04
WDR36 run97.750876e-04
In [845]:
gl <- drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:10]
In [846]:
tmp <- plot_df[plot_df$Gene %in% gl,]

tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [847]:
gl
  1. 'TUBB4B'
  2. 'SLC1A5'
  3. 'TRIB3'
  4. 'RRBP1'
  5. 'TPM1'
  6. 'TUBA1B'
  7. 'DAAM1'
  8. 'CD24'
  9. 'KLF5'
  10. 'KLF10'
In [848]:
ridge_p <- ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
  ggridges::geom_density_ridges() +
  ggridges::theme_ridges() + 
  ggsci::scale_fill_npg()+
xlab('SHAP weight')+
ylab('Driver genes')+
ggtitle("Weight through model with different seeds")+
scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   legend.position = 'none',
   panel.grid = element_blank()
      
)
ridge_p
Picking joint bandwidth of 0.000171

Warning message:
“Removed 1 row containing non-finite outside the scale range
(`stat_density_ridges()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
No description has been provided for this image
In [849]:
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_ridge_p.pdf',
       width =16/1.5, height =16/1.5)
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_ridge_p.png',
       width =16/1.5, height =16/1.5)
Picking joint bandwidth of 0.000171

Warning message:
“Removed 1 row containing non-finite outside the scale range
(`stat_density_ridges()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
Picking joint bandwidth of 0.000171

Warning message:
“Removed 1 row containing non-finite outside the scale range
(`stat_density_ridges()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [850]:
drivers_exp <- fread(file.path(read_dir,'driver_summary_shap_total_addexp.csv'))
In [851]:
head(drivers_exp)
A data.table: 6 × 13
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwaytime0_exptime3_exptime7_exptime14_exp
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl><dbl><dbl>
TUBB4B0.0037623360.0004508041 0.01396089710FALSEFALSEFALSEFALSE 0.02809229-0.3676338-0.2782917 0.274052020
SLC1A50.0024535770.0003867980 0.00812183910FALSEFALSEFALSEFALSE 0.10378601-0.1310962-0.2516212 0.120953940
TRIB3 0.0019458530.0002947887-0.006270785 8FALSEFALSEFALSEFALSE 0.24430865 0.1077166-0.1700614-0.066484846
RRBP1 0.0018945640.0004346339 0.00712235210FALSEFALSEFALSEFALSE-0.21148947-0.2455256 0.5670268 0.009739888
TPM1 0.0018060400.0003299156 0.00861467610FALSEFALSEFALSEFALSE-0.77242580 0.3227159 0.4304303-0.109920340
TUBA1B0.0015047600.0003803525 0.00787245710FALSEFALSEFALSEFALSE 1.04742420-0.4294127-0.4524028 0.104396370
In [852]:
drivers_exp_long<-drivers_exp %>%as.data.frame %>% 
    mutate(direction=ifelse(weight_grad_total_dir_mean > 0,'pos','neg')) %>%
    mutate(rank=rank(-drivers_exp$weight_shap_total_mean)) %>%
    mutate(top20 = ifelse(rank<=20,TRUE,FALSE)) %>%
    mutate(top10 = ifelse(rank<=10,TRUE,FALSE)) %>%
    dplyr::select(c('V1','time0_exp','time3_exp','time7_exp','time14_exp','direction','top20','top10')) %>%
    reshape2::melt(id.vars = c("V1",'direction','top20','top10'), #需保留的不参与聚合的变量列名
                  measure.vars = c('time0_exp','time3_exp','time7_exp','time14_exp'),#需要聚合的变量s1-s10
                  variable.name = c('time_point'),#聚合变量的新列名
                  value.name = 'exp')#聚合值的新列名
In [853]:
head(drivers_exp_long)
A data.frame: 6 × 6
V1directiontop20top10time_pointexp
<chr><chr><lgl><lgl><fct><dbl>
1TUBB4BposTRUETRUEtime0_exp 0.02809229
2SLC1A5posTRUETRUEtime0_exp 0.10378601
3TRIB3 negTRUETRUEtime0_exp 0.24430865
4RRBP1 posTRUETRUEtime0_exp-0.21148947
5TPM1 posTRUETRUEtime0_exp-0.77242580
6TUBA1BposTRUETRUEtime0_exp 1.04742420
In [854]:
pos_drivers <- drivers_exp_long %>% filter(direction=='pos')
neg_drivers <- drivers_exp_long %>% filter(direction=='neg')
In [855]:
pos_time_p<-ggplot(pos_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
  geom_xspline(spline_shape = -0.5)+
  geom_xspline(data = pos_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
  ggrepel::geom_text_repel(data=pos_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
                  aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
  geom_point(size=3)+
  geom_point(data = pos_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
  xlab('Time points (Days)')+
  ylab('Normalized expression')+
  ggtitle('Time-course gene expression')+
  theme_bw()+
  scale_color_manual(values = c('TRUE'= '#259CA2BB','FALSE'='#00000033'))+
  scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
  #ylim(-1.5,1.5)+
  scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.6,1.6))+
  theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5),
        axis.title.y = element_text(vjust = 6),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
       axis.text.x = element_text(vjust = -2),
        axis.text.y = element_text(hjust = -1.5),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(-10,'points'),
       axis.line = element_line(linewidth = 1),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
       panel.border = element_rect(linewidth = 1.5),
       panel.grid = element_blank(),
       #panel.grid.major.x =element_line(),
       legend.position = 'None')


pos_time_p
No description has been provided for this image
In [551]:
ggsave(plot = pos_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_pos_driver_cross_time_ggalt.pdf',
       width =16/1.5, height =9/1.5)
ggsave(plot = pos_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_pos_driver_cross_time_ggalt.png',
       width =16/1.5, height =9/1.5)
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_x_spline()`).”
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).”
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_x_spline()`).”
Warning message:
“Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).”
In [856]:
neg_time_p<-ggplot(neg_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
  geom_xspline(spline_shape = -0.5)+
  geom_xspline(data = neg_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
  ggrepel::geom_text_repel(data=neg_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
                  aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
  geom_point(size=3)+
  geom_point(data = neg_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
  xlab('Time points (Days)')+
  ylab('Normalized expression')+
  ggtitle('Time-course gene expression')+
  theme_bw()+
  scale_color_manual(values = c('TRUE'= '#E0A965BB','FALSE'='#00000033'))+
  scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
  scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.7,1.7))+
  theme(axis.title = element_text(face = 'bold',size=20),
        axis.title.x = element_text(vjust = -5),
        axis.title.y = element_text(vjust = 6),
       axis.text = element_text(face = 'italic',size=18,colour = 'black'),
       axis.text.x = element_text(vjust = -2),
        axis.text.y = element_text(hjust = -1.5),
        axis.ticks = element_line(linewidth = 1.5),
       axis.ticks.length = unit(-10,'points'),
       axis.line = element_line(linewidth = 1),
       plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
       plot.margin = margin(50,50,50,50),
       panel.border = element_rect(linewidth = 1.5),
       panel.grid = element_blank(),
       #panel.grid.major.x =element_line(),
       legend.position = 'None')


neg_time_p
No description has been provided for this image
In [553]:
ggsave(plot = neg_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_neg_driver_cross_time_ggalt.pdf',
       width =16/1.5, height =9/1.5)
ggsave(plot = neg_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_neg_driver_cross_time_ggalt.png',
       width =16/1.5, height =9/1.5)
In [ ]:

In [858]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
'select()' returned 1:1 mapping between keys and columns

In [859]:
kk <- enrichKEGG(gene         = gene$ENTREZID,
                 #keyType      = 'uniprot',
                 organism     = 'hsa',
                 pvalueCutoff = 0.1)
head(kk)
A data.frame: 6 × 11
categorysubcategoryIDDescriptionGeneRatioBgRatiopvaluep.adjustqvaluegeneIDCount
<chr><chr><chr><chr><chr><chr><dbl><dbl><dbl><chr><int>
hsa05016Human Diseases Neurodegenerative disease hsa05016Huntington disease 6/24306/87760.00013532400.0036189330.0027987455704/7052/10376/10383/7277/84086
hsa04814Cellular ProcessesCell motility hsa04814Motor proteins 5/24193/87760.00014771160.0036189330.0027987457168/10376/10383/4430/7277 5
hsa05014Human Diseases Neurodegenerative disease hsa05014Amyotrophic lateral sclerosis 6/24364/87760.00034821320.0056874820.0043984825704/6432/10376/10383/7277/84086
hsa04540Cellular ProcessesCellular community - eukaryoteshsa04540Gap junction 3/2488/8776 0.00169326120.0183687720.01420571010376/10383/7277 3
hsa05130Human Diseases Infectious disease: bacterial hsa05130Pathogenic Escherichia coli infection4/24198/87760.00187436450.0183687720.01420571010376/10383/4430/7277 4
hsa05010Human Diseases Neurodegenerative disease hsa05010Alzheimer disease 5/24384/87760.00333394610.0272272270.0210565025704/10376/10383/7277/8408 5
In [860]:
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
In [861]:
em <- enricher(names(gs), 
           TERM2GENE=m_t2g,
          minGSSize    = 0,
              #maxGSSize    = 500,
              pvalueCutoff = 1,
              #scoreType = "pos"
              qvalueCutoff = 1,
              #eps = eps,
              #verbose      = FALSE
              )
head(em)
A data.frame: 6 × 9
IDDescriptionGeneRatioBgRatiopvaluep.adjustqvaluegeneIDCount
<chr><chr><chr><chr><dbl><dbl><dbl><chr><int>
Mtorc1 SignalingMtorc1 Signaling Mtorc1 Signaling 5/21200/43830.0021039420.028403210.02436143928/5704/6510/57761/7277 5
Tnfa Signaling Via NfkbTnfa Signaling Via NfkbTnfa Signaling Via Nfkb5/21200/43830.0021039420.028403210.024361431052/7071/23764/10769/102215
Estrogen Response EarlyEstrogen Response EarlyEstrogen Response Early4/21200/43830.0136616950.122955260.105458707071/11031/7022/7052 4
Tgf Beta SignalingTgf Beta Signaling Tgf Beta Signaling 2/2154/4383 0.0269394160.181841060.155965047071/11031 2
Cholesterol HomeostasisCholesterol HomeostasisCholesterol Homeostasis2/2174/4383 0.0480221150.229418890.19677254928/57761 2
Il2 Stat5 SignalingIl2 Stat5 Signaling Il2 Stat5 Signaling 3/21199/43830.0671612530.229418890.1967725423764/6510/7052 3
In [862]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()

for (i in 1:nrow(em@result)){
    if (i==1){
        
        ls <- em@geneSets[em@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
        
    }else{
        
    ls <- em@geneSets[em@result[i,'ID']]
    where <- which(high_df$ENTREZID %in% ls[[1]])
    tmp <- high_df[where,]
    tmp$gs <- gs_name[i]
    tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
    high_plot_df <- rbind(high_plot_df,tmp)
    high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
        
    }
}

high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

length(table(high_plot_df$gs))
table(high_plot_df$gs)
27
                     Adipogenesis           Cholesterol Homeostasis 
                                1                                 2 
                      Coagulation                        Complement 
                                2                                 1 
                       Dna Repair                       E2f Targets 
                                1                                 1 
Epithelial Mesenchymal Transition           Estrogen Response Early 
                                2                                 4 
           Estrogen Response Late                    G2m Checkpoint 
                                3                                 2 
                       Glycolysis                   Heme Metabolism 
                                1                                 1 
                          Hypoxia               Il2 Stat5 Signaling 
                                2                                 3 
          Il6 Jak Stat3 Signaling                 Kras Signaling Dn 
                                1                                 1 
                Kras Signaling Up                   Mitotic Spindle 
                                2                                 1 
                 Mtorc1 Signaling                    Myc Targets V1 
                                5                                 3 
                       Myogenesis                       P53 Pathway 
                                1                                 2 
          Pi3k Akt Mtor Signaling                Tgf Beta Signaling 
                                1                                 2 
          Tnfa Signaling Via Nfkb                    Uv Response Up 
                                5                                 1 
            Xenobiotic Metabolism 
                                1 
In [348]:
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
add
A data.frame: 29 × 2
weight_shap_total_meangs
<dbl><chr>
721.284743e-03HALLMARK_TGF_BETA_SIGNALING
1719.876715e-04HALLMARK_TGF_BETA_SIGNALING
2611.945853e-03HALLMARK_CHOLESTEROL_HOMEOSTASIS
315.934694e-05HALLMARK_CHOLESTEROL_HOMEOSTASIS
1025.009635e-04HALLMARK_COAGULATION
335.934694e-05HALLMARK_COAGULATION
241.806040e-03HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION
2328.014264e-04HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION
2218.638531e-04HALLMARK_G2M_CHECKPOINT
15.255194e-04HALLMARK_G2M_CHECKPOINT
2338.014264e-04HALLMARK_HYPOXIA
1035.009635e-04HALLMARK_HYPOXIA
2512.708228e-04HALLMARK_KRAS_SIGNALING_UP
141.475505e-04HALLMARK_KRAS_SIGNALING_UP
2621.945853e-03HALLMARK_P53_PATHWAY
1317.297564e-04HALLMARK_P53_PATHWAY
345.934694e-05HALLMARK_IL6_JAK_STAT3_SIGNALING
2631.945853e-03HALLMARK_PI3K_AKT_MTOR_SIGNALING
61.071527e-04HALLMARK_DNA_REPAIR
2814.270763e-04HALLMARK_UV_RESPONSE_UP
2824.270763e-04HALLMARK_MITOTIC_SPINDLE
1922.453577e-03HALLMARK_ADIPOGENESIS
1045.009635e-04HALLMARK_COMPLEMENT
115.255194e-04HALLMARK_E2F_TARGETS
1529.237856e-04HALLMARK_GLYCOLYSIS
51.481077e-03HALLMARK_HEME_METABOLISM
125.255194e-04HALLMARK_KRAS_SIGNALING_DN
81.301767e-03HALLMARK_MYOGENESIS
1932.453577e-03HALLMARK_XENOBIOTIC_METABOLISM
In [349]:
high_plot_df <- rbind(high_plot_df,add,add)
length(table(high_plot_df$gs))
table(high_plot_df$gs)
27
                     HALLMARK_ADIPOGENESIS 
                                         3 
          HALLMARK_CHOLESTEROL_HOMEOSTASIS 
                                         6 
                      HALLMARK_COAGULATION 
                                         6 
                       HALLMARK_COMPLEMENT 
                                         3 
                       HALLMARK_DNA_REPAIR 
                                         3 
                      HALLMARK_E2F_TARGETS 
                                         3 
HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION 
                                         6 
          HALLMARK_ESTROGEN_RESPONSE_EARLY 
                                         4 
           HALLMARK_ESTROGEN_RESPONSE_LATE 
                                         3 
                   HALLMARK_G2M_CHECKPOINT 
                                         6 
                       HALLMARK_GLYCOLYSIS 
                                         3 
                  HALLMARK_HEME_METABOLISM 
                                         3 
                          HALLMARK_HYPOXIA 
                                         6 
              HALLMARK_IL2_STAT5_SIGNALING 
                                         3 
          HALLMARK_IL6_JAK_STAT3_SIGNALING 
                                         3 
                HALLMARK_KRAS_SIGNALING_DN 
                                         3 
                HALLMARK_KRAS_SIGNALING_UP 
                                         6 
                  HALLMARK_MITOTIC_SPINDLE 
                                         3 
                 HALLMARK_MTORC1_SIGNALING 
                                         5 
                   HALLMARK_MYC_TARGETS_V1 
                                         3 
                       HALLMARK_MYOGENESIS 
                                         3 
                      HALLMARK_P53_PATHWAY 
                                         6 
          HALLMARK_PI3K_AKT_MTOR_SIGNALING 
                                         3 
               HALLMARK_TGF_BETA_SIGNALING 
                                         6 
          HALLMARK_TNFA_SIGNALING_VIA_NFKB 
                                         5 
                   HALLMARK_UV_RESPONSE_UP 
                                         3 
            HALLMARK_XENOBIOTIC_METABOLISM 
                                         3 
In [350]:
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
head(high_plot_df)
A data.frame: 6 × 3
gsweight_shap_total_meanmean
<chr><dbl><dbl>
1HALLMARK_ADIPOGENESIS 0.0024535770.002453577
2HALLMARK_ADIPOGENESIS 0.0024535770.002453577
3HALLMARK_ADIPOGENESIS 0.0024535770.002453577
4HALLMARK_CHOLESTEROL_HOMEOSTASIS0.0019458530.001002600
5HALLMARK_CHOLESTEROL_HOMEOSTASIS0.0019458530.001002600
6HALLMARK_CHOLESTEROL_HOMEOSTASIS0.0019458530.001002600
In [351]:
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
In [352]:
colnames(high_plot_df) <- c('gs','Weight','mean')
In [353]:
pRidge <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = '   ') +
  scale_fill_gradientn(name = "Median",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.text.y = element_text(size = 10,color="black"),
        axis.text.x = element_text(size = 12,color="black"),
       panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank() # get rid of minor grid
       )

pRidge
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.539

No description has been provided for this image
In [354]:
png('weight_pRidge_all_meancolor_t0_c_nc.png',height = 500*5,width = 600*5,res=400)
    pRidge
dev.off()
Picking joint bandwidth of 0.539

png: 2

pRidge

In [863]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
'select()' returned 1:1 mapping between keys and columns

In [864]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>% 
  dplyr::select(gs_name, entrez_gene)

m_t2g$gs_name <- m_t2g$gs_name %>% 
    stringr::str_replace_all(pattern = 'HALLMARK_',replacement = '') %>%
    stringr::str_replace_all(pattern = '_',replacement = ' ') %>%
    stringr::str_to_title()

kk <- enrichKEGG(gene         = names(gs),
                 #keyType      = 'uniprot',
                 organism     = 'hsa',
                 pvalueCutoff = 0.05)
#head(kk)
em <- enricher(names(gs), 
           TERM2GENE=m_t2g,
          minGSSize    = 0,
              #maxGSSize    = 500,
              pvalueCutoff = 1,
              #scoreType = "pos"
              qvalueCutoff = 1,
              #eps = eps,
              #verbose      = FALSE
              )
In [865]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()

for (i in 1:nrow(em@result)){
    if (i==1){
        
        ls <- em@geneSets[em@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
        
    }else{
        
    ls <- em@geneSets[em@result[i,'ID']]
    where <- which(high_df$ENTREZID %in% ls[[1]])
    tmp <- high_df[where,]
    tmp$gs <- gs_name[i]
    tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
    high_plot_df <- rbind(high_plot_df,tmp)
    high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
        
    }
}

high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [866]:
#ggplot2 修改图例的一些操作 https://zhuanlan.zhihu.com/p/166529941
pRidge_H <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = 'Hallmark',fill = 'Log10 mean of weight') +
    xlab('SHAP weight')+
    ylab('Gene set')+
  scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
  scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18), 
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
       )

pRidge_H
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.539

No description has been provided for this image
In [867]:
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_pRidge_H.pdf',
       width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_pRidge_H.png',
       width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.539

Picking joint bandwidth of 0.539

In [868]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- kk@result[1:30,]$Description
high_mean_ls <-c()

for (i in 1:nrow(kk@result[1:30,])){
    if (i==1){
        
        ls <- kk@geneSets[kk@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
        
    }else{
        
    ls <- kk@geneSets[kk@result[i,'ID']]
    where <- which(high_df$ENTREZID %in% ls[[1]])
    tmp <- high_df[where,]
    tmp$gs <- gs_name[i]
    tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
    high_plot_df <- rbind(high_plot_df,tmp)
    high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
        
    }
}

high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [869]:
pRidge_K <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = 'KEGG',fill = 'Log10 mean of weight') +
    xlab('SHAP weight')+
    ylab('Gene set')+
  scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
  scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = 'white'), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18), 
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
       )

pRidge_K
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.52

No description has been provided for this image
In [870]:
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_pRidge_K.pdf',
       width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_pRidge_K.png',
       width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.52

Picking joint bandwidth of 0.52

In [ ]:

build pip¶

In [492]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
read_dir <- file.path(read_dir,run_name)

drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))

drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))

pathway_hallmark = openxlsx::read.xlsx(file.path('./resources/pathway', 'Gene_signature_list_paper_supp.xlsx'))
In [525]:
plot_df$pw = 'Other'
plot_df[plot_df$Gene %in% pathway_hallmark$HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY,]$pw <- 'ROS'
plot_df[plot_df$Gene %in% pathway_hallmark$HALLMARK_FATTY_ACID_METABOLISM,]$pw <- 'FAM'
plot_df$pw <- factor(plot_df$pw ,levels = c('Other','ROS','FAM'))
In [526]:
ggplot(data = plot_df)+
geom_boxplot(aes(x=variable,y=value,fill=pw))+
theme_classic()
Warning message:
“Removed 408 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
No description has been provided for this image
In [404]:
normalize <- function(v) {
  (v - min(v)) / (max(v) - min(v))
}
In [541]:
drivers$norm_shap <-  normalize(drivers$weight_shap_total_mean)
ddf <- drivers[order(drivers$weight_shap_total_mean,decreasing = T),][1:10,] %>% as.data.frame()
ddf$V1 <- factor(ddf$V1,levels = ddf$V1)
In [542]:
ggplot(ddf) +
  geom_bar(aes(x = V1, y = norm_shap),stat = "identity",fill=rgb(0.1,0.4,0.5,0.7))+
    theme_classic()
No description has been provided for this image
In [544]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [545]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
    file_name = paste0('driver_info_',i,'.csv')
    tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
    df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
    
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [563]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
plot_df <- na.omit(plot_df)
plot_df$value <- normalize(plot_df$value)

gl <- ddf$V1
tmp <- plot_df[plot_df$Gene %in% gl,]

tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [564]:
ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
  ggridges::geom_density_ridges() +
  ggridges::theme_ridges() + 
  theme(legend.position = "none")
Picking joint bandwidth of 0.0311

No description has been provided for this image
In [568]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
'select()' returned 1:1 mapping between keys and columns

Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“6.35% of input gene IDs are fail to map...”
In [575]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>% 
  dplyr::select(gs_name, entrez_gene)

kk <- enrichKEGG(gene         = names(gs),
                 #keyType      = 'uniprot',
                 organism     = 'hsa',
                 pvalueCutoff = 0.05)
#head(kk)
em <- enricher(names(gs), 
           TERM2GENE=m_t2g,
          minGSSize    = 0,
              #maxGSSize    = 500,
              pvalueCutoff = 1,
              #scoreType = "pos"
              qvalueCutoff = 1,
              #eps = eps,
              #verbose      = FALSE
              )
In [578]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()

for (i in 1:nrow(em@result)){
    if (i==1){
        
        ls <- em@geneSets[em@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
        
    }else{
        
    ls <- em@geneSets[em@result[i,'ID']]
    where <- which(high_df$ENTREZID %in% ls[[1]])
    tmp <- high_df[where,]
    tmp$gs <- gs_name[i]
    tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
    high_plot_df <- rbind(high_plot_df,tmp)
    high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
        
    }
}

high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [580]:
pRidge <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = '   ') +
  scale_fill_gradientn(name = "Median",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.text.y = element_text(size = 10,color="black"),
        axis.text.x = element_text(size = 12,color="black"),
       panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank() # get rid of minor grid
       )

pRidge
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.487

No description has been provided for this image
In [588]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- kk@result[1:30,]$Description
high_mean_ls <-c()

for (i in 1:nrow(kk@result[1:30,])){
    if (i==1){
        
        ls <- kk@geneSets[kk@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
        
    }else{
        
    ls <- kk@geneSets[kk@result[i,'ID']]
    where <- which(high_df$ENTREZID %in% ls[[1]])
    tmp <- high_df[where,]
    tmp$gs <- gs_name[i]
    tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
    high_plot_df <- rbind(high_plot_df,tmp)
    high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
        
    }
}

high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [589]:
pRidge <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = '   ') +
  scale_fill_gradientn(name = "Median",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.text.y = element_text(size = 10,color="black"),
        axis.text.x = element_text(size = 12,color="black"),
       panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank() # get rid of minor grid
       )

pRidge
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.4

No description has been provided for this image
In [ ]:
pdf('weight_pRidge_all_meancolor_t0_c_nc.pdf',height = 500*5,width = 600*5)
    pRidge
dev.off()
In [167]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% select(c('V1','weight_shap_total'))
In [168]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% select(c('V1','weight_shap_total'))
for (i in 1:9){
    file_name = paste0('driver_info_',i,'.csv')
    tdf <- fread(file.path(read_dir,file_name)) %>% select(c('V1','weight_shap_total'))
    df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
    
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [169]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [668]:
normalize <- function(v) {
  (v - min(v)) / (max(v) - min(v))
}
draw_ridge <- function(em,ags,name,cut=30){
    
    
    high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
    result <- em@result
    if (nrow(result) >=30){
        result <- result[1:30,]
    }
    gs_name <- result$Description
    high_mean_ls <-c()

    for (i in 1:nrow(result)){
        if (i==1){

            ls <- em@geneSets[em@result[i,'ID']]
            where <- which(high_df$ENTREZID %in% ls[[1]])
            tmp <- high_df[where,]
            tmp$gs <- gs_name[i]
            high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
            high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))

        }else{

        ls <- em@geneSets[em@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_plot_df <- rbind(high_plot_df,tmp)
        high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))

        }
    }

    high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

    add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
    high_plot_df <- rbind(high_plot_df,add,add)
    high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
    high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
    high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
    colnames(high_plot_df) <- c('gs','Weight','mean')
    
    pRidge <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
          ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
          labs(title = '   ') +
          scale_fill_gradientn(name = "Median",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
          theme(axis.text.y = element_text(size = 10,color="black"),
                axis.text.x = element_text(size = 12,color="black"),
               panel.background = element_rect(fill = "transparent"), # bg of the panel
        plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
        panel.grid.major = element_blank(), # get rid of major grid
        panel.grid.minor = element_blank() # get rid of minor grid
               )

    #pdf(paste0(name,'_weight_pRidge_all_meancolor.pdf'),height = 5,width = 6)
    #pRidge
    #dev.off()
    ggsave(pRidge,filename = paste0(name,'_weight_pRidge_all_meancolor.pdf'),height = 250/30.48,width = 300/30.48)
}
pipe_n <- function(run_name){
    read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
    #run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
    read_dir <- file.path(read_dir,run_name)

    drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))

    drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
    drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
    print(drivers[drivers$is_in_Pathway,])

    pathway_hallmark = openxlsx::read.xlsx(file.path('./resources/pathway', 'Gene_signature_list_paper_supp.xlsx'))
    tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
    file_name = paste0('driver_info_',0,'.csv')
    df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
    for (i in 1:9){
        file_name = paste0('driver_info_',i,'.csv')
        tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
        df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))

    }
    colnames(df) <- c('Gene',paste0('run',0:9))
    plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
    plot_df$pw = 'Other'
    if (any(drivers$is_in_FAM) & any(drivers$is_in_ROS)){
        plot_df[plot_df$Gene %in% pathway_hallmark$HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY,]$pw <- 'ROS'
        plot_df[plot_df$Gene %in% pathway_hallmark$HALLMARK_FATTY_ACID_METABOLISM,]$pw <- 'FAM'
        plot_df$pw <- factor(plot_df$pw ,levels = c('Other','ROS','FAM'))
        p <- ggplot(data = plot_df)+
            geom_boxplot(aes(x=variable,y=value,fill=pw))+
            theme_classic()
        ggsave(p,filename = paste0(run_name,'_dirver_in_pw.pdf'),height = 5,width = 6)
    }
    drivers$norm_shap <-  normalize(drivers$weight_shap_total_mean)
    ddf <- drivers[order(drivers$weight_shap_total_mean,decreasing = T),][1:10,] %>% as.data.frame()
    ddf$V1 <- factor(ddf$V1,levels = ddf$V1)
    p <- ggplot(ddf) +
        geom_bar(aes(x = V1, y = norm_shap),stat = "identity",fill=rgb(0.1,0.4,0.5,0.7))+
        theme_classic()
    ggsave(p,filename = paste0(run_name,'_dirver_top10.pdf'),height = 5,width = 6)
    
    tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
    file_name = paste0('driver_info_',0,'.csv')
    df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
    for (i in 1:9){
        file_name = paste0('driver_info_',i,'.csv')
        tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
        df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))

    }
    colnames(df) <- c('Gene',paste0('run',0:9))
    plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
    plot_df <- na.omit(plot_df)
    plot_df$value <- normalize(plot_df$value)

    gl <- ddf$V1
    tmp <- plot_df[plot_df$Gene %in% gl,]

    tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
    p <- ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
        ggridges::geom_density_ridges() +
        ggridges::theme_ridges() + 
        theme(legend.position = "none")
    ggsave(p,filename = paste0(run_name,'_dirver_top10_inrun.pdf'),height = 5,width = 5)
    
    gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
    gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
    ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
    gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
    gs <- gs$weight_shap_total_mean
    names(gs) <- ags$ENTREZID
    
    m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>% 
                  dplyr::select(gs_name, entrez_gene)

    kk <- enrichKEGG(gene         = names(gs),
                     #keyType      = 'uniprot',
                     organism     = 'hsa',
                     pvalueCutoff = 0.05)
    #head(kk)
    em <- enricher(names(gs), 
               TERM2GENE=m_t2g,
              minGSSize    = 0,
                  #maxGSSize    = 500,
                  pvalueCutoff = 1,
                  #scoreType = "pos"
                  qvalueCutoff = 1,
                  #eps = eps,
                  #verbose      = FALSE
                  )
    draw_ridge(em,ags,name = paste0(run_name,'_HALLMARK'))
    draw_ridge(kk,ags,name = paste0(run_name,'_KEGG'))
    
}
In [669]:
run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
pipe_n(run_name)
        V1 weight_shap_total_mean weight_shap_total_std
    <char>                  <num>                 <num>
1:   H2AFZ            0.001409551          0.0002377385
2:   PRDX1            0.005753293          0.0005948305
3:    NQO1            0.007189172          0.0005834166
4: ALDH3A1            0.001305538          0.0001966169
   weight_grad_total_dir_mean counts  is_tf is_in_FAM is_in_ROS is_in_Pathway
                        <num>  <int> <lgcl>    <lgcl>    <lgcl>        <lgcl>
1:               4.583457e-05     10  FALSE      TRUE     FALSE          TRUE
2:               1.307083e-04     10  FALSE     FALSE      TRUE          TRUE
3:               1.745988e-04     10  FALSE     FALSE      TRUE          TRUE
4:               4.354592e-07      4  FALSE      TRUE     FALSE          TRUE
   rank_shap_weight rank_grad_weight
              <num>            <num>
1:               37               41
2:                5                9
3:                3                3
4:               39               49
Warning message:
“Removed 408 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Picking joint bandwidth of 0.0311

'select()' returned 1:1 mapping between keys and columns

Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“6.35% of input gene IDs are fail to map...”
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.467

Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.4

In [670]:
run_name <- 'main_PC9_LUNG_run10_t7_t14_cyc'
pipe_n(run_name)
        V1 weight_shap_total_mean weight_shap_total_std
    <char>                  <num>                 <num>
1:   PRDX1            0.003196059          0.0005124655
2:   H2AFZ            0.004075658          0.0005847643
3: ALDH3A1            0.004452039          0.0005462090
4:    NQO1            0.002075033          0.0002167071
   weight_grad_total_dir_mean counts  is_tf is_in_FAM is_in_ROS is_in_Pathway
                        <num>  <int> <lgcl>    <lgcl>    <lgcl>        <lgcl>
1:               0.0004087128     10  FALSE     FALSE      TRUE          TRUE
2:               0.0006475787     10  FALSE      TRUE     FALSE          TRUE
3:               0.0007624793     10  FALSE      TRUE     FALSE          TRUE
4:               0.0003687808      9  FALSE     FALSE      TRUE          TRUE
   rank_shap_weight rank_grad_weight
              <num>            <num>
1:               13               22
2:                8                8
3:                6                5
4:               23               24
Warning message:
“Removed 398 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Picking joint bandwidth of 0.021

'select()' returned 1:1 mapping between keys and columns

Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“1.67% of input gene IDs are fail to map...”
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.344

Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.265

In [671]:
run_name <- 'main_PC9_LUNG_run10_t14_ncyc_cyc'
pipe_n(run_name)
       V1 weight_shap_total_mean weight_shap_total_std
   <char>                  <num>                 <num>
1:  H2AFZ           0.0015434791          0.0003817574
2:   JUNB           0.0016835371          0.0008096141
3:   SOD2           0.0002215576          0.0000000000
4: LGALS1           0.0001387697          0.0000000000
5:   PFKP           0.0002421256          0.0000000000
   weight_grad_total_dir_mean counts  is_tf is_in_FAM is_in_ROS is_in_Pathway
                        <num>  <int> <lgcl>    <lgcl>    <lgcl>        <lgcl>
1:               0.0015961908     10  FALSE      TRUE     FALSE          TRUE
2:              -0.0029814138      7   TRUE     FALSE      TRUE          TRUE
3:               0.0007589676      1  FALSE     FALSE      TRUE          TRUE
4:               0.0001830989      1  FALSE      TRUE     FALSE          TRUE
5:               0.0002091714      1  FALSE     FALSE      TRUE          TRUE
   rank_shap_weight rank_grad_weight
              <num>            <num>
1:               22               20
2:               20                9
3:               63               39
4:               72               65
5:               61               63
Warning message:
“Removed 666 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Picking joint bandwidth of 0.0387

'select()' returned 1:1 mapping between keys and columns

Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“4.94% of input gene IDs are fail to map...”
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.326

Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.276

In [672]:
run_name <- 'main_PC9_LUNG_run10_t0_ncyc_cyc'
pipe_n(run_name)
Empty data.table (0 rows and 11 cols): V1,weight_shap_total_mean,weight_shap_total_std,weight_grad_total_dir_mean,counts,is_tf...
Picking joint bandwidth of 0.0387

'select()' returned 1:1 mapping between keys and columns

Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.539

Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.52

In [673]:
run_name <- 'main_PC9_LUNG_run10_t0_sen_res'
pipe_n(run_name)
Empty data.table (0 rows and 11 cols): V1,weight_shap_total_mean,weight_shap_total_std,weight_grad_total_dir_mean,counts,is_tf...
Picking joint bandwidth of 0.049

'select()' returned 1:1 mapping between keys and columns

Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“3.85% of input gene IDs are fail to map...”
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.588

Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.592

In [674]:
run_name <- 'ex_PC9_LUNG_run10_ncyc_cyc'
pipe_n(run_name)
       V1 weight_shap_total_mean weight_shap_total_std
   <char>                  <num>                 <num>
1:   JUNB            0.001167002          0.0003537751
   weight_grad_total_dir_mean counts  is_tf is_in_FAM is_in_ROS is_in_Pathway
                        <num>  <int> <lgcl>    <lgcl>    <lgcl>        <lgcl>
1:              -0.0005070834     10   TRUE     FALSE      TRUE          TRUE
   rank_shap_weight rank_grad_weight
              <num>            <num>
1:                5               16
Picking joint bandwidth of 0.0559

'select()' returned 1:1 mapping between keys and columns

Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.422

Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.413

In [675]:
run_name <- 'ex_HCC827_LUNG_run10_ncyc_cyc'
pipe_n(run_name)
Empty data.table (0 rows and 11 cols): V1,weight_shap_total_mean,weight_shap_total_std,weight_grad_total_dir_mean,counts,is_tf...
Warning message:
“Removed 2 rows containing missing values or values outside the scale range
(`geom_bar()`).”
Picking joint bandwidth of 0.0206

'select()' returned 1:1 mapping between keys and columns

Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.666

Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.704

In [676]:
run_name <- 'main_PC9_LUNG_run10_t0_sen_res_strict'
pipe_n(run_name)
Empty data.table (0 rows and 11 cols): V1,weight_shap_total_mean,weight_shap_total_std,weight_grad_total_dir_mean,counts,is_tf...
Picking joint bandwidth of 0.0422

'select()' returned 1:1 mapping between keys and columns

Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“3.33% of input gene IDs are fail to map...”
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.539

Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.465

In [ ]:

add new func¶

沙漠中的岩石图片

In [460]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
read_dir <- file.path(read_dir,run_name)
In [461]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
FN1 0.0079933780.00075376071.907695e-0410FALSEFALSEFALSEFALSE
HIST1H2BD0.0038648270.00050085941.813786e-0410FALSEFALSEFALSEFALSE
CCND1 0.0023067440.00027282801.020445e-0410FALSEFALSEFALSEFALSE
KRT8 0.0041713730.00060779777.349687e-0510FALSEFALSEFALSEFALSE
GSTM3 0.0065702270.00047768011.470234e-0410FALSEFALSEFALSEFALSE
CD9 0.0042869820.00044312041.179431e-0410FALSEFALSEFALSEFALSE
In [462]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [463]:
drivers[drivers$is_in_Pathway,]
A data.table: 4 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
H2AFZ 0.0014095510.00023773854.583457e-0510FALSE TRUEFALSETRUE3741
PRDX1 0.0057532930.00059483051.307083e-0410FALSEFALSE TRUETRUE 5 9
NQO1 0.0071891720.00058341661.745988e-0410FALSEFALSE TRUETRUE 3 3
ALDH3A10.0013055380.00019661694.354592e-07 4FALSE TRUEFALSETRUE3949
In [466]:
drivers %>% mutate(direction = ifelse(weight_grad_total_dir_mean >= 0,1,-1)) %>% filter(counts >=10)
A data.table: 28 × 12
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weightdirection
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl><dbl>
FN1 0.0079933780.0007537607 1.907695e-0410FALSEFALSEFALSEFALSE 2 1 1
HIST1H2BD0.0038648270.0005008594 1.813786e-0410FALSEFALSEFALSEFALSE14 2 1
CCND1 0.0023067440.0002728280 1.020445e-0410FALSEFALSEFALSEFALSE2718 1
KRT8 0.0041713730.0006077977 7.349687e-0510FALSEFALSEFALSEFALSE1230 1
GSTM3 0.0065702270.0004776801 1.470234e-0410FALSEFALSEFALSEFALSE 4 8 1
CD9 0.0042869820.0004431204 1.179431e-0410FALSEFALSEFALSEFALSE1014 1
H2AFZ 0.0014095510.0002377385 4.583457e-0510FALSE TRUEFALSE TRUE3741 1
LAPTM4A 0.0055573910.0006989103 1.191966e-0410FALSEFALSEFALSEFALSE 613 1
MDK 0.0044854940.0007073663 9.334373e-0510FALSEFALSEFALSEFALSE 923 1
PRDX1 0.0057532930.0005948305 1.307083e-0410FALSEFALSE TRUE TRUE 5 9 1
PGK1 0.0029370070.0004105406 8.659618e-0510FALSEFALSEFALSEFALSE2025 1
NQO1 0.0071891720.0005834166 1.745988e-0410FALSEFALSE TRUE TRUE 3 3 1
ALPP 0.0037903640.0003220504 1.560096e-0410FALSEFALSEFALSEFALSE15 6 1
MT2A 0.0029928150.0007070334-5.982608e-0510FALSEFALSEFALSEFALSE1936-1
PKM 0.0024184760.0004418046 8.013423e-0510FALSEFALSEFALSEFALSE2627 1
UBB 0.0020930290.0004443382-5.899295e-0510FALSEFALSEFALSEFALSE3037-1
FOSL1 0.0042657690.0005058976-9.429040e-0510 TRUEFALSEFALSEFALSE1121-1
CTSA 0.0040279710.0005851937 1.295741e-0410FALSEFALSEFALSEFALSE1310 1
UBE2S 0.0047840570.0005292148-1.516910e-0410FALSEFALSEFALSEFALSE 8 7-1
HSPB1 0.0020172960.0003566292 7.373099e-0510FALSEFALSEFALSEFALSE3129 1
MYL6 0.0085084580.0008462955 1.682113e-0410FALSEFALSEFALSEFALSE 1 4 1
TCOF1 0.0022865290.0003146553-6.928714e-0510FALSEFALSEFALSEFALSE2832-1
TFDP1 0.0033304280.0003013794-9.927206e-0510 TRUEFALSEFALSEFALSE1719-1
CSTB 0.0031482890.0003750370-1.198390e-0410FALSEFALSEFALSEFALSE1812-1
JUND 0.0016843240.0003056773 1.057126e-0410 TRUEFALSEFALSEFALSE3317 1
TGM2 0.0053295820.0004317851-1.178054e-0410FALSEFALSEFALSEFALSE 715-1
SQSTM1 0.0028696040.0004758771 9.440278e-0510FALSEFALSEFALSEFALSE2120 1
HIST1H4C 0.0037515880.0004830967-1.076414e-0410FALSEFALSEFALSEFALSE1616-1
In [ ]:

ext lung¶

In [119]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'ex_PC9_LUNG_run10_ncyc_cyc'
read_dir <- file.path(read_dir,run_name)
In [120]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
KRT8 0.00290126160.0003359210 0.001363648410FALSEFALSEFALSEFALSE
CCND1 0.00059430840.0002472813 0.000280469110FALSEFALSEFALSEFALSE
IGFBP30.00176757490.0005854681-0.000451521010FALSEFALSEFALSEFALSE
FOS 0.00089790960.0003130372-0.000593716910 TRUEFALSEFALSEFALSE
NDRG1 0.00090673070.0004197639-0.000518058510FALSEFALSEFALSEFALSE
MCM7 0.00145323260.0003902143-0.000746614610FALSEFALSEFALSEFALSE
In [65]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [66]:
drivers[drivers$is_in_Pathway,]
A data.table: 1 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
JUNB0.0011670020.0003537751-0.000507083410TRUEFALSETRUETRUE516
In [80]:
drivers[order(weight_shap_total_mean,decreasing = T),]
A data.table: 46 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
KRT8 2.901262e-033.359210e-04 1.363648e-0310FALSEFALSEFALSEFALSE 1 1
IGFBP3 1.767575e-035.854681e-04-4.515210e-0410FALSEFALSEFALSEFALSE 218
SVIP 1.605107e-032.204604e-04-6.380635e-04 9FALSEFALSEFALSEFALSE 3 8
MCM7 1.453233e-033.902143e-04-7.466146e-0410FALSEFALSEFALSEFALSE 4 4
JUNB 1.167002e-033.537751e-04-5.070834e-0410 TRUEFALSE TRUE TRUE 516
LMO7 1.146797e-034.774872e-04-3.694111e-0410FALSEFALSEFALSEFALSE 620
TGM2 1.072440e-033.278384e-04-9.264323e-0510FALSEFALSEFALSEFALSE 728
NDRG1 9.067307e-044.197639e-04-5.180585e-0410FALSEFALSEFALSEFALSE 815
FOS 8.979096e-043.130372e-04-5.937169e-0410 TRUEFALSEFALSEFALSE 911
TANK 8.252715e-044.200121e-04-7.183048e-04 9FALSEFALSEFALSEFALSE10 6
CEBPD 7.429256e-041.433738e-04-7.833183e-0410 TRUEFALSEFALSEFALSE11 3
E2F1 6.951700e-043.402180e-04 6.151628e-04 8 TRUEFALSEFALSEFALSE12 9
HMOX1 6.298396e-041.902128e-04-5.331388e-0410FALSEFALSEFALSEFALSE1314
CCND1 5.943084e-042.472813e-04 2.804691e-0410FALSEFALSEFALSEFALSE1424
CDKN1A 5.431046e-048.991307e-05-8.614744e-04 9FALSEFALSEFALSEFALSE15 2
SIAH1 3.769688e-045.853661e-04-6.948847e-05 4FALSEFALSEFALSEFALSE1630
IVNS1ABP3.549956e-042.229496e-04-4.345496e-04 5FALSEFALSEFALSEFALSE1719
SLC1A5 3.495283e-041.844679e-04 1.458682e-05 5FALSEFALSEFALSEFALSE1838
FAR1 3.422899e-049.989324e-05-8.674871e-05 5FALSEFALSEFALSEFALSE1929
WWTR1 3.397314e-041.384696e-04-4.099336e-05 6FALSEFALSEFALSEFALSE2032
NDC80 3.321227e-041.593288e-04 3.184734e-04 6FALSEFALSEFALSEFALSE2122
CTSB 3.319458e-041.892530e-04-1.008085e-05 4FALSEFALSEFALSEFALSE2239
KLF4 3.295498e-049.477489e-05-1.281867e-0410 TRUEFALSEFALSEFALSE2326
PLK1 2.943991e-041.814412e-04 5.684625e-04 4FALSEFALSEFALSEFALSE2413
NFIA 2.905949e-041.358932e-04 4.548551e-04 6 TRUEFALSEFALSEFALSE2517
PTEN 2.753436e-042.635258e-04-8.389234e-06 6FALSEFALSEFALSEFALSE2641
SLC38A2 2.667867e-042.679345e-04-3.606024e-05 3FALSEFALSEFALSEFALSE2734
TNIK 2.456670e-041.031096e-04-7.062921e-04 5FALSEFALSEFALSEFALSE28 7
KIF20B 2.255165e-045.346994e-05 7.188357e-04 2FALSEFALSEFALSEFALSE29 5
MDM2 2.236497e-045.577028e-05 5.878168e-04 5FALSEFALSEFALSEFALSE3012
GATA2 2.138830e-046.709211e-05-3.728669e-05 7 TRUEFALSEFALSEFALSE3133
TCF7L2 2.135133e-045.082929e-05 2.495285e-04 7 TRUEFALSEFALSEFALSE3225
PLEKHA5 2.059156e-044.903499e-05-9.970697e-06 3FALSEFALSEFALSEFALSE3340
SPRY4 1.766548e-040.000000e+00 6.104017e-04 1FALSEFALSEFALSEFALSE3410
THRA 1.487272e-041.311744e-04-4.438621e-05 3 TRUEFALSEFALSEFALSE3531
PRDM5 1.449123e-040.000000e+00 2.015269e-05 1 TRUEFALSEFALSEFALSE3635
ORMDL3 1.250970e-041.241226e-04-1.564841e-05 2FALSEFALSEFALSEFALSE3737
KLF10 1.225750e-041.520613e-04-3.630311e-04 2 TRUEFALSEFALSEFALSE3821
LMO2 1.053465e-048.979617e-05-7.769726e-06 3FALSEFALSEFALSEFALSE3942
MYLK 1.041125e-041.079196e-05 1.929550e-05 2FALSEFALSEFALSEFALSE4036
CCNT2 1.026096e-047.186726e-05 5.367263e-06 2FALSEFALSEFALSEFALSE4143
HOXB5 9.432974e-050.000000e+00-2.272554e-09 1 TRUEFALSEFALSEFALSE4246
CHD7 8.325684e-054.784245e-05 2.934812e-04 3FALSEFALSEFALSEFALSE4323
ETS2 8.007617e-052.313154e-05-1.115904e-04 2 TRUEFALSEFALSEFALSE4427
MAP2K6 6.597339e-050.000000e+00 9.907152e-08 1FALSEFALSEFALSEFALSE4544
PLD1 4.533708e-050.000000e+00 3.604083e-09 1FALSEFALSEFALSEFALSE4645
In [68]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [69]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
    file_name = paste0('driver_info_',i,'.csv')
    tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
    df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
    
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [70]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [71]:
plot_df
A data.frame: 720 × 3
Genevariablevalue
<chr><chr><dbl>
AXIN2 run0 NA
CCND1 run06.757519e-04
CCNT2 run0 NA
CDKN1A run06.160467e-04
CEBPD run07.550301e-04
CHD7 run02.850595e-04
CIITA run07.475761e-05
COL4A2 run02.771913e-05
CTSB run09.563458e-04
CTSK run0 NA
E2F1 run06.434934e-04
ETS2 run09.410978e-05
FAR1 run0 NA
FGFR2 run0 NA
FOS run04.278489e-04
GATA2 run01.472863e-04
GJB1 run02.190515e-06
GLDC run02.539224e-06
HMOX1 run07.792052e-04
HNF4A run01.452764e-05
HOXB5 run0 NA
IGFBP3 run02.464074e-03
IGFBP7 run0 NA
IKZF2 run0 NA
IVNS1ABPrun0 NA
JUNB run01.247005e-03
KIF20B run0 NA
KLF10 run0 NA
KLF2 run0 NA
KLF4 run02.538671e-04
⋮⋮⋮
NCOA2 run92.135522e-04
NDC80 run94.875279e-04
NDRG1 run91.536830e-03
NFIA run94.054250e-04
ORMDL3 run9 NA
PIP5K1Brun9 NA
PLA2G4Arun9 NA
PLD1 run91.339342e-04
PLEKHA5run96.623127e-04
PLK1 run93.379647e-04
PRDM5 run92.193218e-05
PTEN run92.891620e-04
SBF2 run9 NA
SESN1 run9 NA
SIAH1 run9 NA
SLC1A5 run98.802202e-04
SLC38A2run96.103450e-04
SOX5 run91.068104e-05
SPRY4 run9 NA
SVIP run91.850812e-03
TANK run96.539742e-04
TCF7L2 run93.776996e-04
TGM2 run97.247475e-04
THRA run99.682434e-05
TLE4 run9 NA
TNIK run92.857012e-04
TRPS1 run9 NA
TTC30A run9 NA
TTC30B run9 NA
WWTR1 run94.324673e-04
In [880]:
gl <- drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:10]
In [881]:
tmp <- plot_df[plot_df$Gene %in% gl,]

tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [882]:
gl
  1. 'KRT8'
  2. 'IGFBP3'
  3. 'SVIP'
  4. 'MCM7'
  5. 'JUNB'
  6. 'LMO7'
  7. 'TGM2'
  8. 'NDRG1'
  9. 'FOS'
  10. 'TANK'
In [883]:
ridge_p <- ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
  ggridges::geom_density_ridges() +
  ggridges::theme_ridges() + 
  ggsci::scale_fill_npg()+
xlab('SHAP weight')+
ylab('Driver genes')+
ggtitle("Weight through model with different seeds")+
scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   legend.position = 'none',
   panel.grid = element_blank()
      
)
ridge_p
Picking joint bandwidth of 0.00019

Warning message:
“Removed 2 rows containing non-finite outside the scale range
(`stat_density_ridges()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
No description has been provided for this image
In [884]:
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/ext_PC9_cyc_ridge_p.pdf',
       width =16/1.5, height =16/1.5)
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/ext_PC9_cyc_ridge_p.png',
       width =16/1.5, height =16/1.5)
Picking joint bandwidth of 0.00019

Warning message:
“Removed 2 rows containing non-finite outside the scale range
(`stat_density_ridges()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
Picking joint bandwidth of 0.00019

Warning message:
“Removed 2 rows containing non-finite outside the scale range
(`stat_density_ridges()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [72]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")

'select()' returned 1:1 mapping between keys and columns

In [73]:
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
In [78]:
em <- GSEA(gs*1000, 
           TERM2GENE=m_t2g,
          minGSSize    = 0,
              #maxGSSize    = 500,
              pvalueCutoff = 1,
              scoreType = "pos"
              #qvalueCutoff = 1,
              #eps = eps,
              #verbose      = FALSE
              )
head(em)
preparing geneSet collections...

GSEA analysis...

leading edge analysis...

done...

A data.frame: 6 × 11
IDDescriptionsetSizeenrichmentScoreNESpvaluep.adjustqvaluerankleading_edgecore_enrichment
<chr><chr><int><dbl><dbl><dbl><dbl><dbl><dbl><chr><chr>
HALLMARK_NOTCH_SIGNALINGHALLMARK_NOTCH_SIGNALING HALLMARK_NOTCH_SIGNALING 20.96851051.8783090.0053912360.19408450.1940845 1tags=50%, list=2%, signal=51% 595
HALLMARK_INFLAMMATORY_RESPONSEHALLMARK_INFLAMMATORY_RESPONSE HALLMARK_INFLAMMATORY_RESPONSE 10.95555561.9385090.0649350650.44355640.4435564 3tags=100%, list=7%, signal=96%1026
HALLMARK_INTERFERON_GAMMA_RESPONSEHALLMARK_INTERFERON_GAMMA_RESPONSEHALLMARK_INTERFERON_GAMMA_RESPONSE10.95555561.9385090.0649350650.44355640.4435564 3tags=100%, list=7%, signal=96%1026
HALLMARK_APOPTOSISHALLMARK_APOPTOSIS HALLMARK_APOPTOSIS 30.84309721.6152840.0619380620.44355640.4435564 3tags=67%, list=7%, signal=67% 595/1026
HALLMARK_ANDROGEN_RESPONSEHALLMARK_ANDROGEN_RESPONSE HALLMARK_ANDROGEN_RESPONSE 40.80384391.5551360.0739260740.44355640.4435564 1tags=25%, list=2%, signal=27% 595
HALLMARK_TNFA_SIGNALING_VIA_NFKBHALLMARK_TNFA_SIGNALING_VIA_NFKB HALLMARK_TNFA_SIGNALING_VIA_NFKB 90.73269781.4649950.0519480520.44355640.443556410tags=56%, list=22%, signal=54%595/1026/1052/2114/2353
In [79]:
em@result
A data.frame: 36 × 11
IDDescriptionsetSizeenrichmentScoreNESpvaluep.adjustqvaluerankleading_edgecore_enrichment
<chr><chr><int><dbl><dbl><dbl><dbl><dbl><dbl><chr><chr>
HALLMARK_NOTCH_SIGNALINGHALLMARK_NOTCH_SIGNALING HALLMARK_NOTCH_SIGNALING 20.968510481.87830900.0053912360.19408450.1940845 1tags=50%, list=2%, signal=51% 595
HALLMARK_INFLAMMATORY_RESPONSEHALLMARK_INFLAMMATORY_RESPONSE HALLMARK_INFLAMMATORY_RESPONSE 10.955555561.93850870.0649350650.44355640.4435564 3tags=100%, list=7%, signal=96% 1026
HALLMARK_INTERFERON_GAMMA_RESPONSEHALLMARK_INTERFERON_GAMMA_RESPONSE HALLMARK_INTERFERON_GAMMA_RESPONSE 10.955555561.93850870.0649350650.44355640.4435564 3tags=100%, list=7%, signal=96% 1026
HALLMARK_APOPTOSISHALLMARK_APOPTOSIS HALLMARK_APOPTOSIS 30.843097181.61528450.0619380620.44355640.4435564 3tags=67%, list=7%, signal=67% 595/1026
HALLMARK_ANDROGEN_RESPONSEHALLMARK_ANDROGEN_RESPONSE HALLMARK_ANDROGEN_RESPONSE 40.803843901.55513630.0739260740.44355640.4435564 1tags=25%, list=2%, signal=27% 595
HALLMARK_TNFA_SIGNALING_VIA_NFKBHALLMARK_TNFA_SIGNALING_VIA_NFKB HALLMARK_TNFA_SIGNALING_VIA_NFKB 90.732697801.46499550.0519480520.44355640.443556410tags=56%, list=22%, signal=54% 595/1026/1052/2114/2353
HALLMARK_COAGULATIONHALLMARK_COAGULATION HALLMARK_COAGULATION 10.888888891.80326390.1348651350.53346650.5334665 6tags=100%, list=13%, signal=89%1508
HALLMARK_COMPLEMENTHALLMARK_COMPLEMENT HALLMARK_COMPLEMENT 10.888888891.80326390.1348651350.53346650.5334665 6tags=100%, list=13%, signal=89%1508
HALLMARK_HEDGEHOG_SIGNALINGHALLMARK_HEDGEHOG_SIGNALING HALLMARK_HEDGEHOG_SIGNALING 10.844444441.71310070.1778221780.53346650.5334665 8tags=100%, list=17%, signal=84%2114
HALLMARK_IL6_JAK_STAT3_SIGNALINGHALLMARK_IL6_JAK_STAT3_SIGNALING HALLMARK_IL6_JAK_STAT3_SIGNALING 10.755555561.53277430.2457542460.53346650.533466512tags=100%, list=26%, signal=76%3162
HALLMARK_MTORC1_SIGNALINGHALLMARK_MTORC1_SIGNALING HALLMARK_MTORC1_SIGNALING 30.782521321.49922760.1338661340.53346650.5334665 3tags=33%, list=7%, signal=33% 1026
HALLMARK_PI3K_AKT_MTOR_SIGNALINGHALLMARK_PI3K_AKT_MTOR_SIGNALING HALLMARK_PI3K_AKT_MTOR_SIGNALING 40.729398601.41111260.1728271730.53346650.5334665 7tags=50%, list=15%, signal=46% 1026/1869
HALLMARK_ESTROGEN_RESPONSE_LATEHALLMARK_ESTROGEN_RESPONSE_LATE HALLMARK_ESTROGEN_RESPONSE_LATE 30.729828441.39827360.2197802200.53346650.533466510tags=67%, list=22%, signal=56% 595/2353
HALLMARK_G2M_CHECKPOINTHALLMARK_G2M_CHECKPOINT HALLMARK_G2M_CHECKPOINT 50.705661981.37500830.1568431570.53346650.5334665 7tags=40%, list=15%, signal=38% 595/1869
HALLMARK_E2F_TARGETSHALLMARK_E2F_TARGETS HALLMARK_E2F_TARGETS 30.715856751.37150530.2327672330.53346650.5334665 3tags=33%, list=7%, signal=33% 1026
HALLMARK_UV_RESPONSE_UPHALLMARK_UV_RESPONSE_UP HALLMARK_UV_RESPONSE_UP 30.697674421.33666990.2627372630.53346650.533466516tags=100%, list=35%, signal=70%2353/3162/3726
HALLMARK_XENOBIOTIC_METABOLISMHALLMARK_XENOBIOTIC_METABOLISM HALLMARK_XENOBIOTIC_METABOLISM 30.695005731.33155700.2667332670.53346650.533466512tags=67%, list=26%, signal=53% 2114/3162
HALLMARK_HYPOXIAHALLMARK_HYPOXIA HALLMARK_HYPOXIA 60.668759771.31982480.1968031970.53346650.533466514tags=67%, list=30%, signal=53% 1026/2353/3162/3486
HALLMARK_GLYCOLYSISHALLMARK_GLYCOLYSIS HALLMARK_GLYCOLYSIS 10.711111111.44261110.2937062940.53432280.534322814tags=100%, list=30%, signal=71%3486
HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAYHALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY 10.666666671.35244790.3116883120.53432280.534322816tags=100%, list=35%, signal=67%3726
HALLMARK_ESTROGEN_RESPONSE_EARLYHALLMARK_ESTROGEN_RESPONSE_EARLY HALLMARK_ESTROGEN_RESPONSE_EARLY 60.607802931.19952400.3086913090.53432280.534322820tags=83%, list=43%, signal=54% 595/2353/7071/9314/3856
HALLMARK_HEME_METABOLISMHALLMARK_HEME_METABOLISM HALLMARK_HEME_METABOLISM 20.661792461.28346650.3366633370.55090360.5509036 6tags=50%, list=13%, signal=45% 1508
HALLMARK_KRAS_SIGNALING_UPHALLMARK_KRAS_SIGNALING_UP HALLMARK_KRAS_SIGNALING_UP 20.613636361.19007360.4135864140.59478980.594789819tags=100%, list=41%, signal=61%3486/9314
HALLMARK_MYOGENESISHALLMARK_MYOGENESIS HALLMARK_MYOGENESIS 40.589303841.14008180.3996004000.59478980.5947898 3tags=25%, list=7%, signal=26% 1026
HALLMARK_PANCREAS_BETA_CELLSHALLMARK_PANCREAS_BETA_CELLS HALLMARK_PANCREAS_BETA_CELLS 10.555555561.12703990.4295704300.59478980.594789821tags=100%, list=46%, signal=56%4005
HALLMARK_P53_PATHWAYHALLMARK_P53_PATHWAY HALLMARK_P53_PATHWAY 60.555586951.09647360.4245754250.59478980.594789812tags=50%, list=26%, signal=42% 1026/2353/3162
HALLMARK_TGF_BETA_SIGNALINGHALLMARK_TGF_BETA_SIGNALING HALLMARK_TGF_BETA_SIGNALING 30.569167571.09046450.4685314690.61781080.617810818tags=67%, list=39%, signal=43% 3726/7071
HALLMARK_MYC_TARGETS_V1HALLMARK_MYC_TARGETS_V1 HALLMARK_MYC_TARGETS_V1 10.488888890.99179510.4805194810.61781080.617810824tags=100%, list=52%, signal=49%
HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITIONHALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITIONHALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION40.385631730.74605270.7332667330.83504000.835040026tags=75%, list=57%, signal=36% 3486/4176/4638
HALLMARK_MYC_TARGETS_V2HALLMARK_MYC_TARGETS_V2 HALLMARK_MYC_TARGETS_V2 10.288888890.58606080.7132867130.83504000.835040015tags=100%, list=33%, signal=69%
HALLMARK_APICAL_JUNCTIONHALLMARK_APICAL_JUNCTION HALLMARK_APICAL_JUNCTION 10.244444440.49589760.7422577420.83504000.835040013tags=100%, list=28%, signal=73%
HALLMARK_UV_RESPONSE_DNHALLMARK_UV_RESPONSE_DN HALLMARK_UV_RESPONSE_DN 10.244444440.49589760.7422577420.83504000.835040013tags=100%, list=28%, signal=73%
HALLMARK_MITOTIC_SPINDLEHALLMARK_MITOTIC_SPINDLE HALLMARK_MITOTIC_SPINDLE 30.302325580.57922360.8021978020.84938590.849385931tags=100%, list=67%, signal=35%10403/5347
HALLMARK_PEROXISOMEHALLMARK_PEROXISOME HALLMARK_PEROXISOME 10.222222220.45081600.7812187810.84938590.849385912tags=100%, list=26%, signal=76%
HALLMARK_ADIPOGENESISHALLMARK_ADIPOGENESIS HALLMARK_ADIPOGENESIS 30.116279070.22277830.9490509490.97616670.976166722tags=100%, list=48%, signal=56%6510/10010
HALLMARK_IL2_STAT5_SIGNALINGHALLMARK_IL2_STAT5_SIGNALING HALLMARK_IL2_STAT5_SIGNALING 40.071428570.13818750.9820179820.98201800.982018020tags=100%, list=43%, signal=62%6510/81848/7052
In [81]:
em <- enricher(names(gs), 
           TERM2GENE=m_t2g,
          minGSSize    = 0,
              #maxGSSize    = 500,
              pvalueCutoff = 1,
              #scoreType = "pos"
              qvalueCutoff = 1,
              #eps = eps,
              #verbose      = FALSE
              )
head(em)
A data.frame: 6 × 9
IDDescriptionGeneRatioBgRatiopvaluep.adjustqvaluegeneIDCount
<chr><chr><chr><chr><dbl><dbl><dbl><chr><int>
HALLMARK_TNFA_SIGNALING_VIA_NFKBHALLMARK_TNFA_SIGNALING_VIA_NFKBHALLMARK_TNFA_SIGNALING_VIA_NFKB9/31200/43836.024759e-060.00021689130.0001648881595/1026/1052/2114/2353/3726/7071/9314/100109
HALLMARK_ESTROGEN_RESPONSE_EARLYHALLMARK_ESTROGEN_RESPONSE_EARLYHALLMARK_ESTROGEN_RESPONSE_EARLY6/31200/43832.366885e-030.02130196550.0161944767595/2353/7071/9314/3856/7052 6
HALLMARK_HYPOXIAHALLMARK_HYPOXIA HALLMARK_HYPOXIA 6/31200/43832.366885e-030.02130196550.01619447671026/2353/3162/3486/10397/7052 6
HALLMARK_P53_PATHWAYHALLMARK_P53_PATHWAY HALLMARK_P53_PATHWAY 6/31200/43832.366885e-030.02130196550.01619447671026/2353/3162/9314/4193/10397 6
HALLMARK_ANDROGEN_RESPONSEHALLMARK_ANDROGEN_RESPONSE HALLMARK_ANDROGEN_RESPONSE 4/31100/43835.001278e-030.03202845960.0243491213595/3856/10397/54407 4
HALLMARK_PI3K_AKT_MTOR_SIGNALINGHALLMARK_PI3K_AKT_MTOR_SIGNALINGHALLMARK_PI3K_AKT_MTOR_SIGNALING4/31105/43835.948489e-030.03202845960.02434912131026/1869/5608/5728 4
In [96]:
em@result
A data.frame: 36 × 9
IDDescriptionGeneRatioBgRatiopvaluep.adjustqvaluegeneIDCount
<chr><chr><chr><chr><dbl><dbl><dbl><chr><int>
Tnfa Signaling Via NfkbTnfa Signaling Via Nfkb Tnfa Signaling Via Nfkb 9/31200/43836.024759e-060.00021689130.0001648881595/1026/1052/2114/2353/3726/7071/9314/100109
Estrogen Response EarlyEstrogen Response Early Estrogen Response Early 6/31200/43832.366885e-030.02130196550.0161944767595/2353/7071/9314/3856/7052 6
HypoxiaHypoxia Hypoxia 6/31200/43832.366885e-030.02130196550.01619447671026/2353/3162/3486/10397/7052 6
P53 PathwayP53 Pathway P53 Pathway 6/31200/43832.366885e-030.02130196550.01619447671026/2353/3162/9314/4193/10397 6
Androgen ResponseAndrogen Response Androgen Response 4/31100/43835.001278e-030.03202845960.0243491213595/3856/10397/54407 4
Pi3k Akt Mtor SignalingPi3k Akt Mtor Signaling Pi3k Akt Mtor Signaling 4/31105/43835.948489e-030.03202845960.02434912131026/1869/5608/5728 4
Tgf Beta SignalingTgf Beta Signaling Tgf Beta Signaling 3/3154/4383 6.227756e-030.03202845960.02434912133726/7071/25937 3
G2m CheckpointG2m Checkpoint G2m Checkpoint 5/31200/43831.213097e-020.05458938690.0415007035595/1869/9585/10403/5347 5
Notch SignalingNotch Signaling Notch Signaling 2/3132/4383 2.104869e-020.08419477430.0640077232595/6934 2
Il2 Stat5 SignalingIl2 Stat5 Signaling Il2 Stat5 Signaling 4/31199/43834.971368e-020.15144202140.115131361310397/6510/81848/7052 4
Epithelial Mesenchymal TransitionEpithelial Mesenchymal TransitionEpithelial Mesenchymal Transition4/31200/43835.048067e-020.15144202140.11513136133486/4176/4638/7052 4
MyogenesisMyogenesis Myogenesis 4/31200/43835.048067e-020.15144202140.11513136131026/3486/4638/25937 4
Uv Response UpUv Response Up Uv Response Up 3/31158/43839.910482e-020.26598117730.20220791262353/3162/3726 3
ApoptosisApoptosis Apoptosis 3/31161/43831.034371e-010.26598117730.2022079126595/1026/3162 3
Mitotic SpindleMitotic Spindle Mitotic Spindle 3/31199/43831.643245e-010.29890468190.22723747759585/10403/5347 3
AdipogenesisAdipogenesis Adipogenesis 3/31200/43831.660582e-010.29890468190.22723747754638/6510/10010 3
E2f TargetsE2f Targets E2f Targets 3/31200/43831.660582e-010.29890468190.22723747751026/4176/5347 3
Estrogen Response LateEstrogen Response Late Estrogen Response Late 3/31200/43831.660582e-010.29890468190.2272374775595/2353/9314 3
Mtorc1 SignalingMtorc1 Signaling Mtorc1 Signaling 3/31200/43831.660582e-010.29890468190.22723747751026/5347/6510 3
Xenobiotic MetabolismXenobiotic Metabolism Xenobiotic Metabolism 3/31200/43831.660582e-010.29890468190.22723747752114/3162/6510 3
Hedgehog SignalingHedgehog Signaling Hedgehog Signaling 1/3136/4383 2.262870e-010.38792056870.29491037382114 1
Pancreas Beta CellsPancreas Beta Cells Pancreas Beta Cells 1/3140/4383 2.481300e-010.40603087020.30867843944005 1
Reactive Oxygen Species PathwayReactive Oxygen Species Pathway Reactive Oxygen Species Pathway 1/3149/4383 2.951168e-010.46192194540.35116873043726 1
Myc Targets V2Myc Targets V2 Myc Targets V2 1/3158/4383 3.392562e-010.50888431610.38687111755347 1
Heme MetabolismHeme Metabolism Heme Metabolism 2/31200/43834.169591e-010.57732801030.43890433531508/4005 2
Kras Signaling UpKras Signaling Up Kras Signaling Up 2/31200/43834.169591e-010.57732801030.43890433533486/9314 2
Il6 Jak Stat3 SignalingIl6 Jak Stat3 Signaling Il6 Jak Stat3 Signaling 1/3187/4383 4.640314e-010.61870847360.47036316703162 1
PeroxisomePeroxisome Peroxisome 1/31104/43835.262272e-010.67657777430.51435737236477 1
CoagulationCoagulation Coagulation 1/31138/43836.303523e-010.76611911710.58242973811508 1
Uv Response DnUv Response Dn Uv Response Dn 1/31144/43836.462653e-010.76611911710.58242973815728 1
Apical JunctionApical Junction Apical Junction 1/31200/43837.661191e-010.76611911710.58242973815728 1
ComplementComplement Complement 1/31200/43837.661191e-010.76611911710.58242973811508 1
GlycolysisGlycolysis Glycolysis 1/31200/43837.661191e-010.76611911710.58242973813486 1
Inflammatory ResponseInflammatory Response Inflammatory Response 1/31200/43837.661191e-010.76611911710.58242973811026 1
Interferon Gamma ResponseInterferon Gamma Response Interferon Gamma Response 1/31200/43837.661191e-010.76611911710.58242973811026 1
Myc Targets V1Myc Targets V1 Myc Targets V1 1/31200/43837.661191e-010.76611911710.58242973814176 1
In [95]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>% 
  dplyr::select(gs_name, entrez_gene)

m_t2g$gs_name <- m_t2g$gs_name %>% 
    stringr::str_replace_all(pattern = 'HALLMARK_',replacement = '') %>%
    stringr::str_replace_all(pattern = '_',replacement = ' ') %>%
    stringr::str_to_title()

kk <- enrichKEGG(gene         = names(gs),
                 #keyType      = 'uniprot',
                 organism     = 'hsa',
                 pvalueCutoff = 0.05)
#head(kk)
em <- enricher(names(gs), 
           TERM2GENE=m_t2g,
          minGSSize    = 0,
              #maxGSSize    = 500,
              pvalueCutoff = 1,
              #scoreType = "pos"
              qvalueCutoff = 1,
              #eps = eps,
              #verbose      = FALSE
              )
In [109]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()

for (i in 1:nrow(em@result)){
    if (i==1){
        
        ls <- em@geneSets[em@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
        
    }else{
        
    ls <- em@geneSets[em@result[i,'ID']]
    where <- which(high_df$ENTREZID %in% ls[[1]])
    tmp <- high_df[where,]
    tmp$gs <- gs_name[i]
    tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
    high_plot_df <- rbind(high_plot_df,tmp)
    high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
        
    }
}

high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

length(table(high_plot_df$gs))
table(high_plot_df$gs)
36
                     Adipogenesis                 Androgen Response 
                                3                                 4 
                  Apical Junction                         Apoptosis 
                                1                                 3 
                      Coagulation                        Complement 
                                1                                 1 
                      E2f Targets Epithelial Mesenchymal Transition 
                                3                                 4 
          Estrogen Response Early            Estrogen Response Late 
                                6                                 3 
                   G2m Checkpoint                        Glycolysis 
                                5                                 1 
               Hedgehog Signaling                   Heme Metabolism 
                                1                                 2 
                          Hypoxia               Il2 Stat5 Signaling 
                                6                                 4 
          Il6 Jak Stat3 Signaling             Inflammatory Response 
                                1                                 1 
        Interferon Gamma Response                 Kras Signaling Up 
                                1                                 2 
                  Mitotic Spindle                  Mtorc1 Signaling 
                                3                                 3 
                   Myc Targets V1                    Myc Targets V2 
                                1                                 1 
                       Myogenesis                   Notch Signaling 
                                4                                 2 
                      P53 Pathway               Pancreas Beta Cells 
                                6                                 1 
                       Peroxisome           Pi3k Akt Mtor Signaling 
                                1                                 4 
  Reactive Oxygen Species Pathway                Tgf Beta Signaling 
                                1                                 3 
          Tnfa Signaling Via Nfkb                    Uv Response Dn 
                                9                                 1 
                   Uv Response Up             Xenobiotic Metabolism 
                                3                                 3 
In [110]:
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
add
A data.frame: 20 × 2
weight_shap_total_meangs
<dbl><chr>
1105.943084e-04Notch Signaling
422.135133e-04Notch Signaling
828.007617e-05Hedgehog Signaling
211.053465e-04Pancreas Beta Cells
1631.167002e-03Reactive Oxygen Species Pathway
3352.943991e-04Myc Targets V2
63.319458e-04Heme Metabolism
2111.053465e-04Heme Metabolism
1431.767575e-03Kras Signaling Up
1943.295498e-04Kras Signaling Up
1256.298396e-04Il6 Jak Stat3 Signaling
3613.769688e-04Peroxisome
613.319458e-04Coagulation
3512.753436e-04Uv Response Dn
3522.753436e-04Apical Junction
623.319458e-04Complement
1441.767575e-03Glycolysis
3125.431046e-04Inflammatory Response
3135.431046e-04Interferon Gamma Response
2421.453233e-03Myc Targets V1
In [111]:
high_plot_df <- rbind(high_plot_df,add,add)
length(table(high_plot_df$gs))
table(high_plot_df$gs)
36
                     Adipogenesis                 Androgen Response 
                                3                                 4 
                  Apical Junction                         Apoptosis 
                                3                                 3 
                      Coagulation                        Complement 
                                3                                 3 
                      E2f Targets Epithelial Mesenchymal Transition 
                                3                                 4 
          Estrogen Response Early            Estrogen Response Late 
                                6                                 3 
                   G2m Checkpoint                        Glycolysis 
                                5                                 3 
               Hedgehog Signaling                   Heme Metabolism 
                                3                                 6 
                          Hypoxia               Il2 Stat5 Signaling 
                                6                                 4 
          Il6 Jak Stat3 Signaling             Inflammatory Response 
                                3                                 3 
        Interferon Gamma Response                 Kras Signaling Up 
                                3                                 6 
                  Mitotic Spindle                  Mtorc1 Signaling 
                                3                                 3 
                   Myc Targets V1                    Myc Targets V2 
                                3                                 3 
                       Myogenesis                   Notch Signaling 
                                4                                 6 
                      P53 Pathway               Pancreas Beta Cells 
                                6                                 3 
                       Peroxisome           Pi3k Akt Mtor Signaling 
                                3                                 4 
  Reactive Oxygen Species Pathway                Tgf Beta Signaling 
                                3                                 3 
          Tnfa Signaling Via Nfkb                    Uv Response Dn 
                                9                                 3 
                   Uv Response Up             Xenobiotic Metabolism 
                                3                                 3 
In [112]:
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
head(high_plot_df)
A data.frame: 6 × 3
gsweight_shap_total_meanmean
<chr><dbl><dbl>
1Adipogenesis 0.00010411250.0004263041
2Adipogenesis 0.00082527150.0004263041
3Adipogenesis 0.00034952830.0004263041
4Androgen Response0.00290126160.0011672718
5Androgen Response0.00090673070.0011672718
6Androgen Response0.00059430840.0011672718
In [113]:
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
In [114]:
colnames(high_plot_df) <- c('gs','Weight','mean')
In [115]:
pRidge_H <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = 'Hallmark',fill = 'Log10 mean of weight') +
    xlab('SHAP weight')+
    ylab('Gene set')+
  scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
  scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18), 
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
       )

pRidge_H
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.453

No description has been provided for this image
In [334]:
png('weight_pRidge_all_meancolor1.png',height = 500*5,width = 600*5,res=400)
    pRidge
dev.off()
Picking joint bandwidth of 0.322

png: 2

pRidge

In [116]:
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/ext_PC9_cyc_pRidge_H.pdf',
       width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/ext_PC9_cyc_pRidge_H.png',
       width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.453

Picking joint bandwidth of 0.453

In [105]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- kk@result[1:30,]$Description
high_mean_ls <-c()

for (i in 1:nrow(kk@result[1:30,])){
    if (i==1){
        
        ls <- kk@geneSets[kk@result[i,'ID']]
        where <- which(high_df$ENTREZID %in% ls[[1]])
        tmp <- high_df[where,]
        tmp$gs <- gs_name[i]
        high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
        high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
        
    }else{
        
    ls <- kk@geneSets[kk@result[i,'ID']]
    where <- which(high_df$ENTREZID %in% ls[[1]])
    tmp <- high_df[where,]
    tmp$gs <- gs_name[i]
    tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
    high_plot_df <- rbind(high_plot_df,tmp)
    high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
        
    }
}

high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)

add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [106]:
pRidge_K <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
  ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
  labs(title = 'KEGG',fill = 'Log10 mean of weight') +
    xlab('SHAP weight')+
    ylab('Gene set')+
  scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
  scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
  theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = 'white'), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18), 
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
       )

pRidge_K
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.413

No description has been provided for this image
In [107]:
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/ext_PC9_cyc_pRidge_K.pdf',
       width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/ext_PC9_cyc_pRidge_K.png',
       width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.413

Picking joint bandwidth of 0.413

In [ ]:

In [127]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t14_ncyc_cyc'
read_dir <- file.path(read_dir,run_name)
In [128]:
t14_drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(t14_drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
HLA-B 0.0066118370.0014072035-0.00388835810FALSEFALSEFALSEFALSE
H2AFZ 0.0015434790.0003817574 0.00159619110FALSE TRUEFALSE TRUE
UBC 0.0030148710.0006034041-0.00355204510FALSEFALSEFALSEFALSE
IGFBP50.0117281110.0037999116 0.01300407910FALSEFALSEFALSEFALSE
GSTM3 0.0092013450.0016104131 0.00469491910FALSEFALSEFALSEFALSE
SAT1 0.0038243560.0007941552-0.00198726110FALSEFALSEFALSEFALSE
In [137]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_ncyc_cyc'
read_dir <- file.path(read_dir,run_name)

t0_drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(t0_drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
PSMC40.00092378560.0003805723 0.00399682310FALSEFALSEFALSEFALSE
LMO7 0.00085112440.0001933136 0.01870590410FALSEFALSEFALSEFALSE
TGM2 0.00080142640.0002573693 0.00267940110FALSEFALSEFALSEFALSE
CD24 0.00133817590.0002601511 0.00629678810FALSEFALSEFALSEFALSE
TPM1 0.00180603970.0003299156 0.00861467610FALSEFALSEFALSEFALSE
SRSF70.00125054690.0003662743-0.00367301110FALSEFALSEFALSEFALSE
In [143]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
read_dir <- file.path(read_dir,run_name)

t0_14_drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(t0_14_drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
FN1 0.0079933780.00075376071.907695e-0410FALSEFALSEFALSEFALSE
HIST1H2BD0.0038648270.00050085941.813786e-0410FALSEFALSEFALSEFALSE
CCND1 0.0023067440.00027282801.020445e-0410FALSEFALSEFALSEFALSE
KRT8 0.0041713730.00060779777.349687e-0510FALSEFALSEFALSEFALSE
GSTM3 0.0065702270.00047768011.470234e-0410FALSEFALSEFALSEFALSE
CD9 0.0042869820.00044312041.179431e-0410FALSEFALSEFALSEFALSE
In [138]:
head(drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
KRT8 0.00290126160.0003359210 0.001363648410FALSEFALSEFALSEFALSE
CCND1 0.00059430840.0002472813 0.000280469110FALSEFALSEFALSEFALSE
IGFBP30.00176757490.0005854681-0.000451521010FALSEFALSEFALSEFALSE
FOS 0.00089790960.0003130372-0.000593716910 TRUEFALSEFALSEFALSE
NDRG1 0.00090673070.0004197639-0.000518058510FALSEFALSEFALSEFALSE
MCM7 0.00145323260.0003902143-0.000746614610FALSEFALSEFALSEFALSE
In [134]:
intersect(t14_drivers$V1, drivers$V1)
  1. 'CCND1'
  2. 'KLF4'
  3. 'JUNB'
  4. 'CEBPD'
  5. 'GATA2'
  6. 'FOS'
In [133]:
library(ggvenn)
Loading required package: grid

In [144]:
a <- list(T14 = t14_drivers$V1,
          EXT = drivers$V1,
           T0 = t0_drivers$V1,
           'T0-14' = t0_14_drivers$V1)
p1=ggvenn(a, c("T14", "EXT","T0",'T0-14')) 
p1
No description has been provided for this image
In [145]:
ggsave(plot = p1,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/cyc_venn.pdf',
       width =16/1.5, height =16/1.5)
ggsave(plot = p1,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/cyc_venn.png',
       width =16/1.5, height =16/1.5)

sen¶

SEN¶

load GDSC¶

In [274]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_sen_res'
read_dir <- file.path(read_dir,run_name)
In [275]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
CD24 0.00083927350.0003266406-5.656427e-0510FALSEFALSEFALSEFALSE
GTF2B 0.00103246970.0003080479-1.316672e-0410 TRUEFALSEFALSEFALSE
MSH6 0.00166129930.0004270973 7.425508e-0510FALSEFALSEFALSEFALSE
TFDP1 0.00137155780.0005694479-5.785423e-0510 TRUEFALSEFALSEFALSE
MYO1B 0.00101956440.0002886838 1.748662e-0410FALSEFALSEFALSEFALSE
TUBA1B0.00183920790.0003183509-1.412048e-0410FALSEFALSEFALSEFALSE
In [276]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [277]:
drivers[order(drivers$weight_shap_total_mean,decreasing = T),]
A data.table: 26 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weight
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl>
SLC1A5 0.00219760486.430204e-04 1.082030e-0410FALSEFALSEFALSEFALSE 1 8
BARD1 0.00216733606.107930e-04 1.084314e-0410FALSEFALSEFALSEFALSE 2 7
CD9 0.00208977184.177533e-04-1.463574e-04 6FALSEFALSEFALSEFALSE 3 4
DAAM1 0.00207008054.885557e-04 1.041791e-04 4FALSEFALSEFALSEFALSE 410
TUBA1B 0.00183920793.183509e-04-1.412048e-0410FALSEFALSEFALSEFALSE 5 5
MSH6 0.00166129934.270973e-04 7.425508e-0510FALSEFALSEFALSEFALSE 612
TFDP1 0.00137155785.694479e-04-5.785423e-0510 TRUEFALSEFALSEFALSE 718
ANXA2 0.00134275096.148865e-04-7.150776e-05 9FALSEFALSEFALSEFALSE 814
RAB31 0.00114500013.613637e-04-5.959134e-05 8FALSEFALSEFALSEFALSE 916
PLK2 0.00113224025.536364e-04 6.720297e-05 9FALSEFALSEFALSEFALSE1015
GTF2B 0.00103246973.080479e-04-1.316672e-0410 TRUEFALSEFALSEFALSE11 6
MYO1B 0.00101956442.886838e-04 1.748662e-0410FALSEFALSEFALSEFALSE12 3
DGKE 0.00097306222.876285e-04-5.831443e-05 9FALSEFALSEFALSEFALSE1317
PSMC4 0.00092144433.828258e-04-4.451585e-05 9FALSEFALSEFALSEFALSE1420
CD24 0.00083927353.266406e-04-5.656427e-0510FALSEFALSEFALSEFALSE1519
TGM2 0.00074156612.308803e-04 5.989809e-0610FALSEFALSEFALSEFALSE1626
HSPA2 0.00069958642.921626e-04-2.506919e-04 8FALSEFALSEFALSEFALSE17 1
RRBP1 0.00068093032.972012e-04-1.221697e-05 9FALSEFALSEFALSEFALSE1823
TUBB4B 0.00063758482.468592e-04 1.148352e-0510FALSEFALSEFALSEFALSE1924
CEBPD 0.00061215972.758779e-04 7.729752e-05 9 TRUEFALSEFALSEFALSE2011
LMO7 0.00060439602.033280e-04 2.352714e-04 6FALSEFALSEFALSEFALSE21 2
TRIB1 0.00047530062.120645e-04 2.118026e-05 6FALSEFALSEFALSEFALSE2222
HIST1H1C0.00038036971.031536e-04-7.409843e-05 4FALSEFALSEFALSEFALSE2313
KLF5 0.00036248022.617805e-04 1.071805e-04 6 TRUEFALSEFALSEFALSE24 9
TPM1 0.00033375731.718587e-04 2.991494e-05 6FALSEFALSEFALSEFALSE2521
TFAP2C 0.00024000258.345076e-05 1.130995e-05 7 TRUEFALSEFALSEFALSE2625
In [1090]:
read_dir
'/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new//main_PC9_LUNG_run10_t0_sen_res'
In [1091]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [1092]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
    file_name = paste0('driver_info_',i,'.csv')
    tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
    df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
    
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [1093]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [1094]:
plot_df
A data.frame: 320 × 3
Genevariablevalue
<chr><chr><dbl>
ANXA2 run01.034460e-03
BARD1 run01.626940e-03
CD24 run09.526794e-04
CD9 run03.365332e-03
CEBPD run05.203552e-04
DAAM1 run05.510014e-03
DGKE run01.200405e-03
GTF2B run01.464899e-03
HIST1H1Crun05.280489e-04
HSPA2 run02.739355e-04
KLF5 run07.949345e-04
LMO7 run08.052727e-04
MSH6 run01.502611e-03
MYLK run05.435189e-05
MYO1B run01.198787e-03
PLK2 run07.665117e-04
PMAIP1 run0 NA
PSMC4 run01.613015e-03
PTPRK run0 NA
RAB31 run09.188358e-04
RRBP1 run03.582313e-04
SCD5 run0 NA
SLC1A5 run02.652719e-03
TFAP2C run02.591484e-04
TFDP1 run01.149151e-03
TGM2 run06.784385e-04
TPM1 run0 NA
TRIB1 run09.816274e-04
TUBA1B run01.910280e-03
TUBB4B run08.910743e-04
⋮⋮⋮
CD24 run90.0008012542
CD9 run90.0028918168
CEBPD run90.0005259405
DAAM1 run9 NA
DGKE run90.0014180804
GTF2B run90.0007559843
HIST1H1Crun90.0005080224
HSPA2 run90.0011746135
KLF5 run90.0001498502
LMO7 run90.0003305257
MSH6 run90.0021751349
MYLK run90.0000477843
MYO1B run90.0009015045
PLK2 run90.0010625910
PMAIP1 run9 NA
PSMC4 run90.0009927616
PTPRK run9 NA
RAB31 run90.0014492702
RRBP1 run90.0005036239
SCD5 run9 NA
SLC1A5 run90.0029968812
TFAP2C run90.0004165673
TFDP1 run90.0011479995
TGM2 run90.0005497827
TPM1 run90.0004014363
TRIB1 run90.0008981097
TUBA1B run90.0013576953
TUBB4B run90.0004397604
ULK1 run90.0000858164
ZC3HAV1 run90.0003587815
In [1095]:
gl <- drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:10]
In [1096]:
tmp <- plot_df[plot_df$Gene %in% gl,]

tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [1097]:
gl
  1. 'SLC1A5'
  2. 'BARD1'
  3. 'CD9'
  4. 'DAAM1'
  5. 'TUBA1B'
  6. 'MSH6'
  7. 'TFDP1'
  8. 'ANXA2'
  9. 'RAB31'
  10. 'PLK2'
In [1098]:
ridge_p <- ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
  ggridges::geom_density_ridges() +
  ggridges::theme_ridges() + 
  ggsci::scale_fill_npg()+
xlab('SHAP weight')+
ylab('Driver genes')+
ggtitle("Weight through model with different seeds")+
scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5,hjust = 0.5),
    axis.title.y = element_text(vjust = 5,hjust = 0.5),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(vjust = 0.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   legend.position = 'none',
   panel.grid = element_blank()
      
)
ridge_p
Picking joint bandwidth of 0.000273

Warning message:
“Removed 6 rows containing non-finite outside the scale range
(`stat_density_ridges()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
No description has been provided for this image
In [1099]:
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_ridge_p.pdf',
       width =16/1.5, height =16/1.5)
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_ridge_p.png',
       width =16/1.5, height =16/1.5)
Picking joint bandwidth of 0.000273

Warning message:
“Removed 6 rows containing non-finite outside the scale range
(`stat_density_ridges()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
Picking joint bandwidth of 0.000273

Warning message:
“Removed 6 rows containing non-finite outside the scale range
(`stat_density_ridges()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [822]:
drivers_exp <- fread(file.path(read_dir,'driver_summary_shap_total_addexp.csv'))
In [823]:
head(drivers_exp)
A data.table: 6 × 13
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwaytime0_exptime3_exptime7_exptime14_exp
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><dbl><dbl><dbl><dbl>
IGFBP5 0.0117281110.0037999116 0.01300407910FALSEFALSEFALSEFALSE-0.4614117-0.0006751149-0.24896230 0.19265650
GSTM3 0.0092013450.0016104131 0.00469491910FALSEFALSEFALSEFALSE-0.4995057-0.4245171000-0.55196540 0.52083343
HLA-B 0.0066118370.0014072035-0.00388835810FALSEFALSEFALSEFALSE-1.3243920-0.1092498800-0.01360234 0.39492068
CD24 0.0044367600.0006784051 0.00583914010FALSEFALSEFALSEFALSE-1.4788445 0.0453659200 0.24021030 0.27349910
HIST1H4C0.0040689350.0005085617-0.00572802910FALSEFALSEFALSEFALSE 1.5583935-0.4402804400-0.44383672-0.02076823
SAT1 0.0038243560.0007941552-0.00198726110FALSEFALSEFALSEFALSE-1.0865847 0.3337592500 0.26571733 0.01365761
In [3]:
GDSC_exp <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/Cell_line_RMA_proc_basalExp.txt')
In [4]:
head(GDSC_exp)
A data.table: 6 × 1020
GENE_SYMBOLSGENE_titleDATA.906826DATA.687983DATA.910927DATA.1240138DATA.1240139DATA.906792DATA.910688DATA.1240135⋯DATA.753584DATA.907044DATA.998184DATA.908145DATA.1659787DATA.1298157DATA.1480372DATA.1298533DATA.930299DATA.905954.1
<chr><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
TSPAN6 tetraspanin 6 [Source:HGNC Symbol;Acc:11858] 7.632023 7.5486718.7123387.797142 7.7292687.074533 3.285198 6.961606⋯ 7.105637 3.236503 3.038892 8.373223 6.932178 8.441628 8.422922 8.0892553.112333 7.153127
TNMD tenomodulin [Source:HGNC Symbol;Acc:17757] 2.964585 2.7777162.6435082.817923 2.9577392.889677 2.828203 2.874751⋯ 2.798847 2.745137 2.976406 2.852552 2.622630 2.639276 2.879890 2.5211692.870468 2.834285
DPM1 dolichyl-phosphate mannosyltransferase polypeptide 1, catalytic subunit [Source:HGNC Symbol;Acc:3005]10.37955311.8073419.8807339.88347110.4188409.77398710.26438510.205931⋯10.48648610.44295110.31196210.45483010.41847511.46374210.55777710.7927509.87390210.788218
SCYL3 SCY1-like 3 (S. cerevisiae) [Source:HGNC Symbol;Acc:19285] 3.614794 4.0668873.9562304.063701 4.3415004.270903 5.968168 3.715033⋯ 3.696835 4.624013 4.348524 3.858121 3.947561 4.425849 3.550390 4.4433374.266828 4.100493
C1orf112chromosome 1 open reading frame 112 [Source:HGNC Symbol;Acc:25565] 3.380681 3.7324853.2366203.558414 3.8403733.815055 3.011867 3.268449⋯ 3.726833 3.947744 3.806584 3.196988 3.814831 4.384732 4.247189 3.0713593.230197 3.435795
FGR Gardner-Rasheed feline sarcoma viral (v-fgr) oncogene homolog [Source:HGNC Symbol;Acc:3697] 3.324692 3.1524043.2412463.101247 3.0018023.298915 9.565308 3.036333⋯ 3.245301 8.969347 3.562548 3.098083 3.170766 3.229511 3.176336 3.2383053.027742 3.330279
In [5]:
GDSC_compounds <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/screened_compounds_rel_8.5.csv')
In [6]:
GDSC_compounds
A data.table: 621 × 6
DRUG_IDSCREENING_SITEDRUG_NAMESYNONYMSTARGETTARGET_PATHWAY
<int><chr><chr><chr><chr><chr>
1MGHErlotinib Tarceva, RG-1415, CP-358774, OSI-774, Ro-508231, R-1415EGFR EGFR signaling
3MGHRapamycin AY-22989, Sirolimus, WY-090217, Torisel, Rapamune MTORC1 PI3K/MTOR signaling
5MGHSunitinib Sutent, Sunitinib Malate, SU-11248 PDGFR, KIT, VEGFR, FLT3, RET, CSF1RRTK signaling
6MGHPHA-665752 PHA665752, PHA 665752 MET RTK signaling
9MGHMG-132 LLL cpd, MG 132, MG132 Proteasome, CAPN1 Protein stability and degradation
11MGHPaclitaxel BMS-181339-01, Taxol, Onxol, Paxene, Praxel, Abraxane Microtubule stabiliser Mitosis
17MGHCyclopamine SMO Other
29MGHAZ628 AZ-628, AZ 628 BRAF ERK MAPK signaling
30MGHSorafenib Nexavar, 284461-73-0, BAY 43-9006 PDGFR, KIT, VEGFR, RAF RTK signaling
32MGHTozasertib MK 0457,MK-0457,MK-045, VX-680 VX 680 VX-68 AURKA, AURKB, AURKC, others Mitosis
34MGHImatinib Gleevec, STI-571 ABL, KIT, PDGFR Other, kinases
35MGHNVP-TAE684 NVP-TAE 684, TAE684, TAE-684 ALK RTK signaling
37MGHCrizotinib Xalkori, PF2341066, PF-2341066, PF 2341066 MET, ALK, ROS1 RTK signaling
38MGHSaracatinib AZD0530, AZD-0530, AZ-10353926 ABL, SRC Other, kinases
41MGHS-Trityl-L-cysteineNSC 83265, Tritylcysteine KIF11 Mitosis
45MGHZ-LLNle-CHO Z-L-Norleucine-CHO, Gamma-Secretase Inhibitor 1 gamma-secretase Other
51MGHDasatinib BMS-354825-03, BMS-354825, Sprycel ABL, SRC, Ephrins, PDGFR, KIT RTK signaling
52MGHGNF-2 KIN001-013 BCR-ABL ABL signaling
53MGHCGP-60474 KIN001-019, CGP60474, CGP 60474 CDK1,CDK2,CDK5,CDK7,CDK9, PKC Cell cycle
54MGHCGP-082996 CINK4, KIN001-021 CDK4 Cell cycle
55MGHA-770041 KIN001-111 LCK, FYN Other, kinases
56MGHWH-4-023 KIN001-112 SRC, LCK Other, kinases
59MGHWZ-1-84 KIN001-123 BMX Other, kinases
60MGHBI-2536 PLK1, PLK2, PLK3 Cell cycle
62MGHBMS-536924 BMS 536924 IGF1R, IR IGF1R signaling
63MGHBMS-509744 KIN001-127, ITK inhibitor ITK Other, kinases
64MGHCMK KIN001-128 RSK2 Other, kinases
71MGHPyrimethamine Daraprim, Chloridine Dihydrofolate reductase (DHFR) Other
83MGHJW-7-52-1 NA MTOR PI3K/MTOR signaling
86MGHA-443654 KIN001-139 AKT1, AKT2, AKT3 PI3K/MTOR signaling
⋮⋮⋮⋮⋮⋮
2107SANGERLJI308 RSK2, RSK1, RSK3 PI3K/MTOR signaling
2109SANGERAZ6102 TNKS1, TNKS2 WNT signaling
2110SANGERGSK591 EPZ015866, GSK3203591 PMRT5 Chromatin histone methylation
2111SANGERVE821 VE 821, VE-821 ATR Genome integrity
2112SANGERVTP-A Unclassified
2113SANGERVTP-B Unclassified
2145SANGERPBD-288 Unclassified
2148SANGERPOMHEX Unclassified
2149SANGERCT7033-2 KDM4A, KDM4C, KDM4E, KDM3A, KDM6BChromatin histone methylation
2154SANGERGSK-LSD1-2HCl LSD1 Chromatin histone methylation
2156SANGER5-azacytidine DNA methyltransferases Other
2157SANGERA-366 EHMT1, EHMT2 Chromatin histone methylation
2158SANGERCPI-637 EP300 Chromatin histone methylation
2159SANGERUNC0379 SETD8 Chromatin histone methylation
2169SANGERAZD6482 AZD 6482, AZD-6482, AK-55409 PI3Kbeta PI3K/MTOR signaling
2170SANGERAT13148 AKT1 PI3K/MTOR signaling
2171SANGERBMS-754807 BMS-754807 IGF1R, IR IGF1R signaling
2172SANGERJQ1 JQ-1, (+)-JQ-1 BRD2, BRD3, BRD4, BRDT Chromatin other
2173SANGERPFI-1 BRD4 Chromatin other
2174SANGERIOX2 IOX-2, IOX 2, AK176060 EGLN1 Other
2175SANGERCHIR-99021 CT 99021, CHIR99021, CHIR 99021GSK3A, GSK3B WNT signaling
2177SANGERSGC0946 DOT1L Chromatin histone methylation
2359SANGERGSK2830371 None WIP1 Other
2360SANGERTHR-101 WIMM synthesis Mutant RAS PI3K/MTOR signaling
2361SANGERTHR-102 WIMM synthesis Mutant RAS PI3K/MTOR signaling
2362SANGERTHR-103 WIMM synthesis Mutant RAS PI3K/MTOR signaling
2438SANGERascorbate (vitamin C)back-up solution from YWKim anti-oxidant proteins Other
2439SANGERglutathione G6013, sigma anti-oxidant proteins Other
2498SANGERalpha-lipoic acid aLA Metabolism Metabolism
2499SANGERN-acetyl cysteine NAC Metabolism Metabolism
In [7]:
GDSC_cellline <- openxlsx::read.xlsx('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/Cell_Lines_Details.xlsx')
In [8]:
head(GDSC_cellline)
A data.frame: 6 × 13
Sample.NameCOSMIC.identifierWhole.Exome.Sequencing.(WES)Copy.Number.Alterations.(CNA)Gene.ExpressionMethylationDrug.ResponseGDSC.Tissue.descriptor.1GDSC.Tissue.descriptor.2Cancer.Type.(matching.TCGA.label)Microsatellite.instability.Status.(MSI)Screen.MediumGrowth.Properties
<chr><dbl><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
1A253 906794YYYYYaero_dig_tracthead and neckNA MSS/MSI-LD/F12Adherent
2BB30-HNC 753531YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
3BB49-HNC 753532YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
4BHY 753535YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
5BICR10 1290724YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
6BICR22 1240121YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
In [9]:
GDSC_drug1 <- openxlsx::read.xlsx('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/GDSC1_fitted_dose_response_27Oct23.xlsx')
In [10]:
GDSC_drug1
A data.frame: 333161 × 19
DATASETNLME_RESULT_IDNLME_CURVE_IDCOSMIC_IDCELL_LINE_NAMESANGER_MODEL_IDTCGA_DESCDRUG_IDDRUG_NAMEPUTATIVE_TARGETPATHWAY_NAMECOMPANY_IDWEBRELEASEMIN_CONCMAX_CONCLN_IC50AUCRMSEZ_SCORE
<chr><dbl><dbl><dbl><chr><chr><chr><dbl><chr><chr><chr><dbl><chr><dbl><dbl><dbl><dbl><dbl><dbl>
1GDSC134215580432684057ES5 SIDM00263UNCLASSIFIED1ErlotinibEGFREGFR signaling1045Y0.0078132 3.9668130.9856780.026081 1.299144
2GDSC134215580806684059ES7 SIDM00269UNCLASSIFIED1ErlotinibEGFREGFR signaling1045Y0.0078132 2.6920900.9726900.110059 0.156076
3GDSC134215581198684062EW-11 SIDM00203UNCLASSIFIED1ErlotinibEGFREGFR signaling1045Y0.0078132 2.4779900.9444590.087019-0.035912
4GDSC134215581542684072SK-ES-1 SIDM01111UNCLASSIFIED1ErlotinibEGFREGFR signaling1045Y0.0078132 2.0335640.9507580.016290-0.434437
5GDSC134215581930687448COLO-829 SIDM00909SKCM 1ErlotinibEGFREGFR signaling1045Y0.0078132 2.9660070.9547780.180255 0.401702
6GDSC1342155850596875628-MG-BA SIDM00998GBM 1ErlotinibEGFREGFR signaling1045Y0.0078132 2.4939430.9802000.041896-0.021607
7GDSC134215585789687568GB-1 SIDM00581GBM 1ErlotinibEGFREGFR signaling1045Y0.0078132 3.6032430.9846240.069293 0.973124
8GDSC134215586874687590U-87-MG SIDM01189GBM 1ErlotinibEGFREGFR signaling1045Y0.0078132 3.8756740.9826050.030822 1.217418
9GDSC134215587948687600NCI-H720 SIDM01120UNCLASSIFIED1ErlotinibEGFREGFR signaling1045Y0.0078132 3.6942130.9860850.026592 1.054699
10GDSC134215590086687799NCI-H1648SIDM00746LUAD 1ErlotinibEGFREGFR signaling1045Y0.0078132-3.1303150.3499720.044001-5.064987
11GDSC134215590484687800NCI-H1650SIDM00745LUAD 1ErlotinibEGFREGFR signaling1045Y0.0078132 3.6618430.9832980.033401 1.025671
12GDSC134215591111687804NCI-H1770SIDM00737UNCLASSIFIED1ErlotinibEGFREGFR signaling1045Y0.0078132 3.2592820.9882180.025678 0.664688
13GDSC134215591503687807NCI-H1838SIDM00769LUAD 1ErlotinibEGFREGFR signaling1045Y0.0078132 2.3228380.9312090.057085-0.175040
14GDSC134215594378687983DMS-114 SIDM00865SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 3.9200060.9882470.025773 1.257172
15GDSC134215595456687997NCI-H1092SIDM00653SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 3.6814960.9833160.041623 1.043295
16GDSC134215595843688006NCI-H1694SIDM00741SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 3.2952880.9879240.025592 0.696975
17GDSC134215596237688007NCI-H187 SIDM00767SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 3.4164010.9839640.038981 0.805580
18GDSC134215596624688010NCI-H1963SIDM00760SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 2.7625830.9884470.060202 0.219288
19GDSC134215597141688013NCI-H209 SIDM00706SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 1.7021100.9254300.029840-0.731659
20GDSC134215597525688014NCI-H2141SIDM00699SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 3.1549610.9874160.053634 0.571141
21GDSC134215597920688015NCI-H2171SIDM00733SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 2.6230650.9916850.021532 0.094180
22GDSC134215598138688018NCI-H2227SIDM00730SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 3.4467350.9827870.072259 0.832780
23GDSC134215598448688021NCI-H345 SIDM00719SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 4.1700680.9861410.024686 1.481407
24GDSC134215598841688023NCI-H446 SIDM00965SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 3.0479650.9820640.065505 0.475196
25GDSC134215599234688025NCI-H526 SIDM01128SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 2.7267170.9759800.060841 0.187126
26GDSC134215599619688026NCI-H64 SIDM01126SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 2.6256230.9949980.014885 0.096473
27GDSC134215599907688027NCI-H69 SIDM01121SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 3.9776170.9878720.021174 1.308832
28GDSC134215600286688031NCI-H82 SIDM01131SCLC 1ErlotinibEGFREGFR signaling1045Y0.0078132 1.8441670.9358320.088138-0.604273
29GDSC134215600925688086SK-N-DZ SIDM01100NB 1ErlotinibEGFREGFR signaling1045Y0.0078132 3.1063360.9738600.029073 0.527538
30GDSC134215601314688087SK-N-FI SIDM01099NB 1ErlotinibEGFREGFR signaling1045Y0.0078132 3.1156150.9837880.021956 0.535858
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333132GDSC1342159035951480362OV-56 SIDM00475OV 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.6713070.9881400.032001 0.262398
333133GDSC1342159039031480364OVCA420 SIDM00967OV 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063103.5660440.9308600.038729-1.334749
333134GDSC1342159042111480367OVCA433 SIDM00966OV 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.5093330.9771660.069618 0.028339
333135GDSC1342159045181480371OVK-18 SIDM00238OV 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.9715700.9771570.091919 0.696290
333136GDSC1342159048241480372PEO1 SIDM00472OV 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.4207220.9760400.026130-0.099707
333137GDSC1342159051311480374UWB1.289 SIDM00815OV 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063105.5122900.9751350.042982 1.477652
333138GDSC1342159057411503362OACp4C SIDM00445ESCA 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.5431850.9728740.074415 0.077257
333139GDSC1342159060441503363OACM5-1 SIDM00444ESCA 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063105.0023700.9822630.206642 0.740797
333140GDSC1342159063511503364SK-GT-2 SIDM00393STAD 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.7571950.9819760.057134 0.386510
333141GDSC1342159065251503365SK-GT-4 SIDM00483ESCA 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.8044240.9724380.030300 0.454758
333142GDSC1342159068351503366ESO26 SIDM00539ESCA 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.8100520.9867400.025473 0.462891
333143GDSC1342159071451503367ESO51 SIDM00538ESCA 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063105.4203670.9771010.072332 1.344820
333144GDSC1342159074031503368KYAE-1 SIDM00530ESCA 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.4610810.9788630.031596-0.041387
333145GDSC1342159077051503369EMC-BAC-1SIDM00048LUAD 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063105.0601630.9785740.042571 0.824310
333146GDSC1342159080111503370EMC-BAC-2SIDM00047LUAD 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.0507140.9615890.071150-0.634382
333147GDSC1342159083161503371TE-4 SIDM00250ESCA 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.5246540.9864040.080202 0.050479
333148GDSC1342159086231509073NCC010 SIDM00231KIRC 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.6171980.9784940.095539 0.184209
333149GDSC1342159087941509074NCC021 SIDM00232KIRC 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.4513140.9744220.029674-0.055500
333150GDSC1342159091011524414RCC-FG2 SIDM00819KIRC 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063105.1403180.9781360.042883 0.940137
333151GDSC1342159094091524415RCC-JF SIDM00818KIRC 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.7980950.9803770.044352 0.445612
333152GDSC1342159097171524416RCC-JW SIDM00817KIRC 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063103.4705320.9497290.017115-1.472768
333153GDSC1342159100221524417RCC-ER SIDM00820KIRC 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.1278390.9584090.042696-0.522934
333154GDSC1342159103291524418RCC-AB SIDM00821KIRC 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.7225470.9708210.030351 0.336442
333155GDSC1342159106311524419RCC-MF SIDM00816KIRC 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.2845910.9573270.057606-0.296421
333156GDSC1342159109341659817KMS-11 SIDM00608MM 1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063102.7586040.9342450.063815-2.501531
333157GDSC1342159113771659823SNU-1040 SIDM00217COREAD1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063105.0852940.9722510.040661 0.860626
333158GDSC1342159121221660035SNU-61 SIDM00194COREAD1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063105.7253990.9761090.045453 1.785602
333159GDSC1342159124311660036SNU-81 SIDM00193COREAD1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.9307530.9708510.038612 0.637308
333160GDSC1342159127391674021SNU-C5 SIDM00498COREAD1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.5517840.9723300.042649 0.089683
333161GDSC1342159130461789883DiFi SIDM00049COREAD1531I-CBP112EP300, CBPChromatin histone acetylation1005Y0.039063104.6810690.9809060.015312 0.276505
In [11]:
GDSC_drug2 <- openxlsx::read.xlsx('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/GDSC2_fitted_dose_response_27Oct23.xlsx')
In [12]:
GDSC_drug2
A data.frame: 242036 × 19
DATASETNLME_RESULT_IDNLME_CURVE_IDCOSMIC_IDCELL_LINE_NAMESANGER_MODEL_IDTCGA_DESCDRUG_IDDRUG_NAMEPUTATIVE_TARGETPATHWAY_NAMECOMPANY_IDWEBRELEASEMIN_CONCMAX_CONCLN_IC50AUCRMSEZ_SCORE
<chr><dbl><dbl><dbl><chr><chr><chr><dbl><chr><chr><chr><dbl><chr><dbl><dbl><dbl><dbl><dbl><dbl>
1GDSC234315946310683667PFSK-1 SIDM01132MB 1003CamptothecinTOP1DNA replication1046Y1e-040.1-1.4638870.9302200.089052 0.433123
2GDSC234315946548684052A673 SIDM00848UNCLASSIFIED1003CamptothecinTOP1DNA replication1046Y1e-040.1-4.8694550.6149700.111351-1.421100
3GDSC234315946830684057ES5 SIDM00263UNCLASSIFIED1003CamptothecinTOP1DNA replication1046Y1e-040.1-3.3605860.7910720.142855-0.599569
4GDSC234315947087684059ES7 SIDM00269UNCLASSIFIED1003CamptothecinTOP1DNA replication1046Y1e-040.1-5.0449400.5926600.135539-1.516647
5GDSC234315947369684062EW-11 SIDM00203UNCLASSIFIED1003CamptothecinTOP1DNA replication1046Y1e-040.1-3.7419910.7340470.128059-0.807232
6GDSC234315947651684072SK-ES-1 SIDM01111UNCLASSIFIED1003CamptothecinTOP1DNA replication1046Y1e-040.1-5.1429610.5824390.137581-1.570016
7GDSC234315947932687448COLO-829 SIDM00909SKCM 1003CamptothecinTOP1DNA replication1046Y1e-040.1-1.2350340.8673480.093470 0.557727
8GDSC2343159482126874525637 SIDM00807BLCA 1003CamptothecinTOP1DNA replication1046Y1e-040.1-2.6326320.8340670.076169-0.203221
9GDSC234315948491687455RT4 SIDM01085BLCA 1003CamptothecinTOP1DNA replication1046Y1e-040.1-2.9631910.8214380.094466-0.383200
10GDSC234315948772687457SW780 SIDM01160BLCA 1003CamptothecinTOP1DNA replication1046Y1e-040.1-1.4491380.9050500.074109 0.441154
11GDSC234315949053687459TCCSUP SIDM01190BLCA 1003CamptothecinTOP1DNA replication1046Y1e-040.1-2.3506330.8434300.074831-0.049682
12GDSC234315949334687505C-33-A SIDM00889CESC 1003CamptothecinTOP1DNA replication1046Y1e-040.1-3.3808790.7778060.091913-0.610618
13GDSC234315949616687506C-4-I SIDM00905CESC 1003CamptothecinTOP1DNA replication1046Y1e-040.1-2.2556900.8911030.087072 0.002012
14GDSC234315949896687514ME-180 SIDM00627CESC 1003CamptothecinTOP1DNA replication1046Y1e-040.1-3.2239100.7866580.135256-0.525153
15GDSC23431595017768756142-MG-BA SIDM00982GBM 1003CamptothecinTOP1DNA replication1046Y1e-040.1-3.4002200.7775170.111615-0.621149
16GDSC2343159504586875628-MG-BA SIDM00998GBM 1003CamptothecinTOP1DNA replication1046Y1e-040.1-4.2562860.6939150.110348-1.087249
17GDSC234315950738687563A172 SIDM00799GBM 1003CamptothecinTOP1DNA replication1046Y1e-040.1-2.9995520.8034070.083111-0.402998
18GDSC234315951019687568GB-1 SIDM00581GBM 1003CamptothecinTOP1DNA replication1046Y1e-040.1-3.0555000.7719000.107697-0.433460
19GDSC234315951300687586T98G SIDM01171GBM 1003CamptothecinTOP1DNA replication1046Y1e-040.1-2.0602180.8887740.075349 0.108440
20GDSC234315951582687588U-118-MG SIDM01193GBM 1003CamptothecinTOP1DNA replication1046Y1e-040.1-3.3540180.7482660.103497-0.595993
21GDSC234315951863687590U-87-MG SIDM01189GBM 1003CamptothecinTOP1DNA replication1046Y1e-040.1-2.4508700.8417920.120575-0.104258
22GDSC234315952116687592YKG-1 SIDM00315GBM 1003CamptothecinTOP1DNA replication1046Y1e-040.1-2.7843590.8240850.091627-0.285832
23GDSC234315952295687596ChaGo-K-1SIDM00924UNCLASSIFIED1003CamptothecinTOP1DNA replication1046Y1e-040.1 0.5288450.9835000.063083 1.518103
24GDSC234315952475687600NCI-H720 SIDM01120UNCLASSIFIED1003CamptothecinTOP1DNA replication1046Y1e-040.1 0.5327920.9846920.041052 1.520252
25GDSC234315952744687777Calu-3 SIDM00922LUAD 1003CamptothecinTOP1DNA replication1046Y1e-040.1-1.6295460.8632430.096562 0.342928
26GDSC234315953026687780COR-L23 SIDM00512UNCLASSIFIED1003CamptothecinTOP1DNA replication1046Y1e-040.1-1.7620800.8823400.081469 0.270767
27GDSC234315953307687787LK-2 SIDM00548LUSC 1003CamptothecinTOP1DNA replication1046Y1e-040.1-1.1576070.9229680.122499 0.599884
28GDSC234315953588687794NCI-H1437SIDM00734LUAD 1003CamptothecinTOP1DNA replication1046Y1e-040.1-1.8395240.9001300.095318 0.228601
29GDSC234315953768687798NCI-H1623SIDM00747LUAD 1003CamptothecinTOP1DNA replication1046Y1e-040.1-4.1359440.6876010.152799-1.021727
30GDSC234315954049687799NCI-H1648SIDM00746LUAD 1003CamptothecinTOP1DNA replication1046Y1e-040.1-1.9622650.8794930.108430 0.161773
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242007GDSC2343161774681331037SU-DHL-6 SIDM00407DLBC 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 9.3550560.9920490.115925-0.731217
242008GDSC2343161777471331038SU-DHL-8 SIDM00423DLBC 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 8.1679340.9685460.173626-2.096806
242009GDSC2343161780281331039SUP-HD1 SIDM00422UNCLASSIFIED2499N-acetyl cysteineMetabolismMetabolism1101Y2.001054200011.0356360.9815190.043595 1.202014
242010GDSC2343161783081331040SUP-M2 SIDM00421UNCLASSIFIED2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 9.2127750.9956580.046115-0.894887
242011GDSC2343161785891331045TK SIDM00323DLBC 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 9.5736530.9898110.149113-0.479756
242012GDSC2343161788711331048VAL SIDM00416DLBC 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 8.2245610.9244140.104771-2.031665
242013GDSC2343161791501331049WIL2-NS SIDM01102UNCLASSIFIED2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 8.1234200.9359630.101519-2.148011
242014GDSC2343161793181331050WSU-DLCL2SIDM00413DLBC 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 9.6943300.9918550.049626-0.340938
242015GDSC2343161797781479987DOV13 SIDM00969OV 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 8.6634520.9443180.078590-1.526793
242016GDSC2343161800591479988Hey SIDM00968OV 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 9.5257120.9350690.087330-0.534905
242017GDSC2343161812391480362OV-56 SIDM00475OV 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 9.4828780.9480780.097027-0.584179
242018GDSC2343161814881480364OVCA420 SIDM00967OV 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 9.6188010.9391000.068242-0.427821
242019GDSC2343161822071480372PEO1 SIDM00472OV 2499N-acetyl cysteineMetabolismMetabolism1101Y2.001054200011.6591880.9743630.082122 1.919309
242020GDSC2343161826691503361FLO-1 SIDM01041ESCA 2499N-acetyl cysteineMetabolismMetabolism1101Y2.001054200010.5338270.9718810.070754 0.624766
242021GDSC2343161829501503362OACp4C SIDM00445ESCA 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 9.9066820.9687460.040929-0.096662
242022GDSC2343161832311503363OACM5-1 SIDM00444ESCA 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 9.3399010.9389080.108254-0.748650
242023GDSC2343161835121503364SK-GT-2 SIDM00393STAD 2499N-acetyl cysteineMetabolismMetabolism1101Y2.001054200010.1855760.9755970.021798 0.224160
242024GDSC2343161837931503365SK-GT-4 SIDM00483ESCA 2499N-acetyl cysteineMetabolismMetabolism1101Y2.001054200011.3622560.9423770.080197 1.577737
242025GDSC2343161840731503366ESO26 SIDM00539ESCA 2499N-acetyl cysteineMetabolismMetabolism1101Y2.001054200010.3157340.9753610.103059 0.373886
242026GDSC2343161843541503367ESO51 SIDM00538ESCA 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 9.4415730.9449780.153800-0.631693
242027GDSC2343161846361503368KYAE-1 SIDM00530ESCA 2499N-acetyl cysteineMetabolismMetabolism1101Y2.001054200010.4902140.9883520.081786 0.574596
242028GDSC2343161850961503370EMC-BAC-2SIDM00047LUAD 2499N-acetyl cysteineMetabolismMetabolism1101Y2.001054200010.5340000.9671920.117754 0.624965
242029GDSC2343161853771503371TE-4 SIDM00250ESCA 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 9.2441550.9681440.081944-0.858790
242030GDSC2343161866341524416RCC-JW SIDM00817KIRC 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 9.5185320.9447650.067012-0.543164
242031GDSC2343161876331659818MM1S SIDM01265MM 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 9.3169590.9882360.155465-0.775041
242032GDSC2343161882421659928SNU-175 SIDM00216COREAD 2499N-acetyl cysteineMetabolismMetabolism1101Y2.001054200010.1270820.9767460.074498 0.156872
242033GDSC2343161886951660034SNU-407 SIDM00214COREAD 2499N-acetyl cysteineMetabolismMetabolism1101Y2.0010542000 8.5763770.9133780.057821-1.626959
242034GDSC2343161889531660035SNU-61 SIDM00194COREAD 2499N-acetyl cysteineMetabolismMetabolism1101Y2.001054200010.5196360.9750010.058090 0.608442
242035GDSC2343161894931674021SNU-C5 SIDM00498COREAD 2499N-acetyl cysteineMetabolismMetabolism1101Y2.001054200010.6945790.9699690.101013 0.809684
242036GDSC2343161897751789883DiFi SIDM00049COREAD 2499N-acetyl cysteineMetabolismMetabolism1101Y2.001054200010.0348250.9669880.089057 0.050746
In [374]:
any(GDSC_drug1$DRUG_NAME == 'Osimertinib')
FALSE
In [375]:
any(GDSC_drug2$DRUG_NAME == 'Osimertinib')
TRUE
In [13]:
GDSC_drug2 %>% dplyr::filter(GDSC_drug2$DRUG_NAME == 'Osimertinib')
A data.frame: 957 × 19
DATASETNLME_RESULT_IDNLME_CURVE_IDCOSMIC_IDCELL_LINE_NAMESANGER_MODEL_IDTCGA_DESCDRUG_IDDRUG_NAMEPUTATIVE_TARGETPATHWAY_NAMECOMPANY_IDWEBRELEASEMIN_CONCMAX_CONCLN_IC50AUCRMSEZ_SCORE
<chr><dbl><dbl><dbl><chr><chr><chr><dbl><chr><chr><chr><dbl><chr><dbl><dbl><dbl><dbl><dbl><dbl>
GDSC234315946501683667PFSK-1 SIDM01132MB 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.2798760.9488510.083039-0.290963
GDSC234315946766684052A673 SIDM00848UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.3725550.9554870.075005-0.218362
GDSC234315947052684057ES5 SIDM00263UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.1678340.9477250.057817-0.378733
GDSC234315947305684059ES7 SIDM00269UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8651740.9699780.024503 0.167538
GDSC234315947587684062EW-11 SIDM00203UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.3350560.9349420.039643-0.247737
GDSC234315947867684072SK-ES-1 SIDM01111UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.6580290.9727900.076845 0.005268
GDSC234315948147687448COLO-829 SIDM00909SKCM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.9189380.9382080.056445 0.993017
GDSC2343159484266874525637 SIDM00807BLCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.4574850.8889550.070200-0.935194
GDSC234315948707687455RT4 SIDM01085BLCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.7845370.9814130.067026 0.887732
GDSC234315948988687457SW780 SIDM01160BLCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.3017370.9589790.058973 0.509525
GDSC234315949269687459TCCSUP SIDM01190BLCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.7562580.9652900.044146 0.865579
GDSC234315949552687505C-33-A SIDM00889CESC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.1892400.8834800.063777-1.145327
GDSC234315949831687506C-4-I SIDM00905CESC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.2765880.9064730.040211-1.076901
GDSC234315950113687514ME-180 SIDM00627CESC 1919OsimertinibEGFREGFR signaling1046Y0.0010011-0.0374130.8603570.026376-1.322878
GDSC23431595039368756142-MG-BA SIDM00982GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.4752810.9493910.042978-0.137890
GDSC2343159506736875628-MG-BA SIDM00998GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.3680880.9824570.082000 0.561502
GDSC234315950954687563A172 SIDM00799GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.5880130.9423000.081864-0.049580
GDSC234315951235687568GB-1 SIDM00581GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.9525660.9755610.104329 1.802722
GDSC234315951518687586T98G SIDM01171GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.8691080.9812810.019718 0.953982
GDSC234315951798687588U-118-MG SIDM01193GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.7960250.9267950.059708 0.113368
GDSC234315952059687590U-87-MG SIDM01189GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.3044010.9588070.034969 0.511612
GDSC234315952260687592YKG-1 SIDM00315GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.6411810.9461040.080434-0.007930
GDSC234315952440687596ChaGo-K-1SIDM00924UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.0416740.9697240.126241 0.305801
GDSC234315952683687600NCI-H720 SIDM01120UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.2716990.9760710.116991 0.485994
GDSC234315952962687777Calu-3 SIDM00922LUAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.1050570.8279930.098212-1.211272
GDSC234315953242687780COR-L23 SIDM00512UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.2726150.9542360.060843 0.486711
GDSC234315953524687787LK-2 SIDM00548LUSC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.4409810.9352440.065657-0.164759
GDSC234315953733687794NCI-H1437SIDM00734LUAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.4669790.9734910.047336 0.638969
GDSC234315953984687798NCI-H1623SIDM00747LUAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.1965950.8681510.052729-1.139565
GDSC234315954155687799NCI-H1648SIDM00746LUAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011-0.7015920.8008710.093298-1.843171
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
GDSC2343161826061503361FLO-1 SIDM01041ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8595030.9458650.059545 0.163096
GDSC2343161828861503362OACp4C SIDM00445ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8227520.9565460.075153 0.134306
GDSC2343161831671503363OACM5-1 SIDM00444ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.2115940.9745480.028010 1.222273
GDSC2343161834481503364SK-GT-2 SIDM00393STAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011-3.0669200.5484530.117406-3.696080
GDSC2343161837301503365SK-GT-4 SIDM00483ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8634690.8827330.073594 0.166202
GDSC2343161840091503366ESO26 SIDM00539ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.2391660.9343990.042767-0.322854
GDSC2343161842901503367ESO51 SIDM00538ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.1579520.9754120.024334 1.180251
GDSC2343161845731503368KYAE-1 SIDM00530ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011-1.5601720.7398310.039288-2.515751
GDSC2343161847821503369EMC-BAC-1SIDM00048LUAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011-0.0425160.8568870.132649-1.326876
GDSC2343161850321503370EMC-BAC-2SIDM00047LUAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.6643140.8863200.090450-0.773171
GDSC2343161853131503371TE-4 SIDM00250ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011-1.7073180.7239210.058785-2.631019
GDSC2343161855231503373U-CH2 SIDM01185NA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.8171280.8874440.082043-0.653462
GDSC2343161857791509073NCC010 SIDM00231KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.5747130.9217360.083888-0.843361
GDSC2343161859591509074NCC021 SIDM00232KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.3387820.9538480.078305 0.538544
GDSC2343161861391524414RCC-FG2 SIDM00819KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.8465490.9116540.134748-0.630415
GDSC2343161863191524415RCC-JF SIDM00818KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.9267480.9236100.066450-0.567591
GDSC2343161865711524416RCC-JW SIDM00817KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.2371970.9541330.037277 0.458967
GDSC2343161867801524417RCC-ER SIDM00820KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.6808200.9566400.019253 0.023121
GDSC2343161869601524418RCC-AB SIDM00821KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.8564230.8885320.088464-0.622681
GDSC2343161871401524419RCC-MF SIDM00816KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.9654170.9647310.062086 0.246064
GDSC2343161873201659817KMS-11 SIDM00608MM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.5764690.9672250.059140-0.058623
GDSC2343161875701659818MM1S SIDM01265MM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.8620970.9678790.008336-0.618235
GDSC2343161877761659819OCI-LY7 SIDM00459DLBC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.9484920.9753690.019785-0.550557
GDSC2343161881781659928SNU-175 SIDM00216COREAD1919OsimertinibEGFREGFR signaling1046Y0.0010011-0.2119490.8579020.056394-1.459603
GDSC2343161883881659929SNU-283 SIDM00215NA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.6195650.9130370.081642-0.808226
GDSC2343161886401660034SNU-407 SIDM00214COREAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.1157100.9199570.050110-0.419564
GDSC2343161889101660035SNU-61 SIDM00194COREAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.3789730.9644790.044681 0.570028
GDSC2343161891771660036SNU-81 SIDM00193COREAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8912710.9757640.057990 0.187981
GDSC2343161894291674021SNU-C5 SIDM00498COREAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.5035270.9576380.057810 0.667600
GDSC2343161897111789883DiFi SIDM00049COREAD1919OsimertinibEGFREGFR signaling1046Y0.0010011-2.0618570.6655440.081299-2.908751
In [14]:
GDSC_cellline %>% dplyr::filter(GDSC_cellline$Gene.Expression == 'Y')
A data.frame: 968 × 13
Sample.NameCOSMIC.identifierWhole.Exome.Sequencing.(WES)Copy.Number.Alterations.(CNA)Gene.ExpressionMethylationDrug.ResponseGDSC.Tissue.descriptor.1GDSC.Tissue.descriptor.2Cancer.Type.(matching.TCGA.label)Microsatellite.instability.Status.(MSI)Screen.MediumGrowth.Properties
<chr><dbl><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
A253 906794YYYYYaero_dig_tracthead and neckNA MSS/MSI-LD/F12Adherent
BB30-HNC 753531YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
BB49-HNC 753532YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
BHY 753535YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
BICR10 1290724YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
BICR22 1240121YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
BICR31 1290725YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
BICR78 1240122YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
Ca9-22 753538YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
CAL-27 910916YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
CAL-33 753541YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
Detroit562 906837YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
DOK 910936YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
FADU 906863YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
H3118 1240140YYYYYaero_dig_tracthead and neckNA MSS/MSI-LR Adherent
HN 907059YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
HO-1-N-1 924111YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
HO-1-u-1 753561YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
HSC-2 753562YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
HSC-3 907061YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
HSC-4 907062YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
JHU-011 1240161YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LR Adherent
JHU-022 1240162YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LR Adherent
KON 1298215YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
KOSC-2 753570YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LR Adherent
LB771-HNC 753583YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
OSC-19 1298362YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
OSC-20 1240196YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LD/F12Adherent
PCI-15A 1240204YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LR Adherent
PCI-30 1298529YYYYYaero_dig_tracthead and neckHNSCMSS/MSI-LR Adherent
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
JHH-4 1240158YNYYYdigestive_system liver LIHC MSS/MSI-LD/F12Adherent
RCC-JF 1524415YYYNYkidney kidney KIRC MSS/MSI-LD/F12Adherent
SW156 1240220YNYYYkidney kidney KIRC MSS/MSI-LD/F12Adherent
HOP-62 905972YYYNYlung_NSCLC lung_NSCLC_adenocarcinomaLUAD MSS/MSI-LR Adherent
PC-3 [JPC-3]1240202YYYNYlung_NSCLC lung_NSCLC_adenocarcinomaLUAD MSS/MSI-LD/F12Adherent
NCI-H740 1331055YYYYNlung_SCLC lung_small_cell_carcinomaSCLC MSS/MSI-LR Suspension
COR-L95 1297439YYYNYlung_SCLC lung_small_cell_carcinomaSCLC MSS/MSI-LR Adherent
NCI-H1304 753599YYYNYlung_SCLC lung_small_cell_carcinomaSCLC MSS/MSI-LD/F12Suspension
SHP-77 724872YYYNYlung_SCLC lung_small_cell_carcinomaSCLC MSS/MSI-LR Semi-Adherent
SW1271 1299062YYYNYlung_SCLC lung_small_cell_carcinomaSCLC MSS/MSI-LD/F12Adherent
D-423MG 946372YYYNYnervous_system glioma GBM MSS/MSI-LD/F12Adherent
COLO-783 1240125YYYNYskin melanoma SKCM MSS/MSI-LR Adherent
Hs633T 1240149YNYYYsoft_tissue fibrosarcoma NA MSS/MSI-LD/F12Adherent
RL95-2 930082YYYNYurogenital_systemendometrium NA MSI-H D/F12Adherent
DOV13 1479987YYYNYurogenital_systemovary OV MSS/MSI-LD/F12Adherent
Hey 1479988YYYNYurogenital_systemovary OV MSS/MSI-LR Adherent
NCI-H660 1330975YYYYNurogenital_systemprostate NA MSS/MSI-LD/F12Suspension
JHU-029 1298156YYYNYaero_dig_tract head and neck HNSC NA R Adherent
SCC90 1299052YYYNYaero_dig_tract head and neck HNSC NA R Adherent
MOLM-16 1330948YYYNYleukemia acute_myeloid_leukaemia LAML NA R Suspension
OCI-LY7 1659819YYYNYlymphoma B_cell_lymphoma DLBC NA D/F12Suspension
KMS-11 1659817YYYNYmyeloma myeloma MM NA R Suspension
CHSA0011 1290767YYYNYbone chondrosarcoma NA NA R Adherent
CHSA0108 1290768YYYNYbone chondrosarcoma NA NA R Adherent
JIMT-1 1298157YYYNYbreast breast BRCA NA D/F12Adherent
CL-34 1290771YYYNYlarge_intestine large_intestine COAD/READNA D/F12Adherent
VMRC-LCD 713869YYYNYlung_NSCLC lung_NSCLC_adenocarcinomaLUAD NA D/F12Adherent
K2 1298160YYYNYskin melanoma SKCM NA D/F12Adherent
STS-0421 1299061YYYNYsoft_tissue soft_tissue_other NA NA R Adherent
KP-2 1298218YNYNYpancreas pancreas PAAD NA D/F12Adherent
In [15]:
luad_Osimertinib <- GDSC_drug2 %>% dplyr::filter(GDSC_drug2$COSMIC_ID %in% substring(colnames(GDSC_exp),6)) %>%
    dplyr::filter(DRUG_NAME == 'Osimertinib') %>%
    dplyr::filter(TCGA_DESC == 'LUAD') 

luad_Osimertinib
A data.frame: 61 × 19
DATASETNLME_RESULT_IDNLME_CURVE_IDCOSMIC_IDCELL_LINE_NAMESANGER_MODEL_IDTCGA_DESCDRUG_IDDRUG_NAMEPUTATIVE_TARGETPATHWAY_NAMECOMPANY_IDWEBRELEASEMIN_CONCMAX_CONCLN_IC50AUCRMSEZ_SCORE
<chr><dbl><dbl><dbl><chr><chr><chr><dbl><chr><chr><chr><dbl><chr><dbl><dbl><dbl><dbl><dbl><dbl>
GDSC234315952962687777Calu-3 SIDM00922LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.1050570.8279930.098212-1.211272
GDSC234315953984687798NCI-H1623SIDM00747LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.1965950.8681510.052729-1.139565
GDSC234315954155687799NCI-H1648SIDM00746LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011-0.7015920.8008710.093298-1.843171
GDSC234315954434687800NCI-H1650SIDM00745LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.4078720.8448570.134400-0.974058
GDSC234315954715687802NCI-H1693SIDM00742LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.1113000.8610170.104960-0.423019
GDSC234315955064687807NCI-H1838SIDM00769LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.4626170.9601850.020880 1.418915
GDSC234315955347687812NCI-H2085SIDM00709LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.7737920.9392890.042010 0.095952
GDSC234315955905687816NCI-H2228SIDM00729LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.0722260.9545290.060727 1.113097
GDSC234315956075687819NCI-H2342SIDM00727LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.6489100.9666160.072543 0.781487
GDSC234315956243687820NCI-H2347SIDM00726LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.5539660.9464250.075520 1.490474
GDSC234315956524687821NCI-H2405SIDM00724LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.3331930.9304230.021137 1.317529
GDSC234315962714713869VMRC-LCD SIDM00320LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.7889540.9680420.053503 0.891192
GDSC234315964063722045NCI-H2030SIDM00715LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.6756240.8817350.057337-0.764311
GDSC234315964314722046NCI-H2122SIDM00702LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.7519530.9352930.041326-0.704518
GDSC234315964523722058NCI-H1734SIDM00739LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8732610.9800540.096387 0.173873
GDSC234315964774722066NCI-H650 SIDM01124LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.8744540.9856840.033043 0.958170
GDSC234315966080724834NCI-H2087SIDM00708LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.4114230.9709480.045980 1.378811
GDSC234315967093724859Calu-6 SIDM00921LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.4815900.9683560.035590 1.433777
GDSC234315967657724866NCI-H1355SIDM00645LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.2281940.9568010.049057 1.235276
GDSC234315967939724868NCI-H1792SIDM00771LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.1935700.9781270.042502 0.424791
GDSC234315968919724873NCI-H2009SIDM00756LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.3737730.9616200.017152 0.565955
GDSC234315969127724874NCI-H2291SIDM00728LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8106790.8981880.063197 0.124848
GDSC234315969377724878SW1573 SIDM01163LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 4.6099790.9779020.047665 2.317715
GDSC234315982957753592LXF-289 SIDM00339LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.3434460.9752250.064421 0.542198
GDSC234315983880753600NCI-H1563SIDM00751LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.8829320.9170990.086269-0.601914
GDSC234315984699753608PC-14 SIDM00237LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011-3.3043650.5180410.157858-3.882086
GDSC234315990460905942NCI-H23 SIDM00138LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.9424270.9880780.042001 1.011417
GDSC234315990920905944NCI-H522 SIDM00116LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8520380.9609710.038513 0.157247
GDSC234315992334905949A549 SIDM00903LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.2129450.9196100.083585-0.343394
GDSC234315996623905967NCI-H322MSIDM00117LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011-1.3807720.7231520.066003-2.375215
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
GDSC234315998008 905972HOP-62 SIDM00133LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.7421710.9559520.045442 0.854544
GDSC234316004576 906791ABC-1 SIDM00494LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.8415050.9695010.076861 0.932359
GDSC234316007374 906805COR-L105 SIDM00513LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011-1.3827190.7074020.069176-2.376740
GDSC234316039604 907786LC-2-ad SIDM00297LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.6393990.9641060.035397-0.009326
GDSC234316055468 908460NCI-H441 SIDM00925LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 5.4599080.9658040.033519 2.983518
GDSC234316056177 908463NCI-H1793 SIDM00755LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.7020490.9549300.049446 1.606477
GDSC234316056460 908465NCI-H358 SIDM00718LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.6459060.8600030.038071-0.787591
GDSC234316057830 908472NCI-H1573 SIDM00749LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.1689000.9827030.024483 1.188827
GDSC234316058113 908473NCI-H1666 SIDM00743LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.4475610.8344630.079597-0.942967
GDSC234316058565 908475NCI-H1755 SIDM00738LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8909450.9400010.085464 0.187726
GDSC234316058847 908476NCI-H1993 SIDM00758LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.0916010.9521260.094151 0.344912
GDSC234316068487 909721SK-LU-1 SIDM01108LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 4.5600970.9697730.032863 2.278639
GDSC234316082794 910399NCI-H838 SIDM01150LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.6369130.9541370.076374-0.011274
GDSC234316092829 910900NCI-H1651 SIDM00744LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.7809430.9617970.056277 0.101554
GDSC234316097910 910931RERF-LC-MS SIDM00355LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.9832890.9686380.037446 0.260064
GDSC234316107124 924244NCI-H1975 SIDM00759LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011-3.2721240.5212020.169023-3.856830
GDSC2343161293151240145HCC-44 SIDM01069LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.4733960.9707960.036816 0.643996
GDSC2343161295881240146HCC-827 SIDM01067LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011-3.5907240.4854250.194687-4.106409
GDSC2343161366821240185NCI-H1944 SIDM00762LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.0887070.9000650.089288-0.440717
GDSC2343161372461240187NCI-H2023 SIDM00753LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.0620890.9657890.032283 1.105156
GDSC2343161377971240190NCI-H3122 SIDM00137LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.1510880.9490500.074977 0.391512
GDSC2343161401551240202PC-3_[JPC-3]SIDM00361LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011-2.6045360.5918820.105147-3.333866
GDSC2343161447091247873H3255 SIDM00046LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011-2.5233540.5940910.078894-3.270271
GDSC2343161505651290908HCC-78 SIDM01068LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.9634380.9012440.049513-0.538849
GDSC2343161575701298347NCI-H1435 SIDM00658LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.2154200.9884400.028504 0.441907
GDSC2343161578481298348NCI-H1568 SIDM00750LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.4691930.8550170.078173-0.926021
GDSC2343161582981298350NCI-H1781 SIDM00754LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011-0.3484100.8242950.055829-1.566501
GDSC2343161623761298537RERF-LC-KJ SIDM00356LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.4152620.9460080.067245-0.184907
GDSC2343161847821503369EMC-BAC-1 SIDM00048LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011-0.0425160.8568870.132649-1.326876
GDSC2343161850321503370EMC-BAC-2 SIDM00047LUAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.6643140.8863200.090450-0.773171
In [16]:
luad_Osimertinib_exp <- GDSC_exp %>% dplyr::select(c(1,which(substring(colnames(GDSC_exp),6) %in% luad_Osimertinib$COSMIC_ID)))
head(luad_Osimertinib_exp)
A data.table: 6 × 62
GENE_SYMBOLSDATA.908460DATA.908465DATA.910399DATA.687816DATA.724873DATA.722045DATA.724868DATA.724866DATA.687798⋯DATA.687819DATA.905942DATA.905944DATA.924244DATA.713869DATA.1298347DATA.1298537DATA.687777DATA.724834DATA.722046
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
TSPAN6 8.663931 8.578107 7.3369147.0774557.9346257.121621 7.2479457.3218656.905705⋯7.544027 7.3196168.827198 7.9236724.3530467.753878 8.2982529.0415627.679445 7.730895
TNMD 3.074756 2.862169 2.7947002.8854223.1540093.039269 2.7044452.7200552.592718⋯3.101391 2.7804442.891027 2.7097552.6520752.672617 2.8007772.8023792.691326 2.691445
DPM1 10.16342710.15088610.8659099.2047219.8941849.88115810.0265579.0253939.826111⋯9.42565310.4824419.71048310.0407509.5489789.61250010.2675179.9742809.82422610.420010
SCYL3 4.965831 5.208023 4.2716414.2568994.7443933.666654 3.6070683.9486454.541889⋯4.517643 3.7343413.997366 3.6478584.1887024.888457 4.2078114.2917784.545904 3.648124
C1orf112 3.655091 3.374075 3.4423243.1127873.1660963.330703 3.3232563.0589933.694343⋯3.740183 3.6515004.008008 3.1166603.4359443.611179 2.9940883.7845004.072171 4.008678
FGR 2.991510 2.984719 3.1772992.9601593.1103123.197060 3.0878213.2637443.245974⋯3.336917 3.1352253.274437 3.3463593.0819633.442439 3.1721853.2722333.293596 3.260190
In [436]:
luad_Osimertinib_exp_SLC1A5 <-  luad_Osimertinib_exp%>% dplyr::filter(GENE_SYMBOLS == 'SLC1A5') %>% t() %>% as.data.frame()
In [437]:
luad_Osimertinib_exp_SLC1A5 <- luad_Osimertinib_exp_SLC1A5 %>% mutate(COSMIC_ID = substring(rownames(luad_Osimertinib_exp_SLC1A5),6))
luad_Osimertinib_exp_SLC1A5 <- luad_Osimertinib_exp_SLC1A5[2:nrow(luad_Osimertinib_exp_SLC1A5),] %>% apply(2,as.numeric)
colnames(luad_Osimertinib_exp_SLC1A5) <- c('SLC1A5','COSMIC_ID')
In [438]:
luad_Osimertinib_exp_SLC1A5
A matrix: 61 × 2 of type dbl
SLC1A5COSMIC_ID
7.975696 908460
8.479559 908465
7.921446 910399
8.292501 687816
8.027455 724873
6.631435 722045
7.023257 724868
6.888380 724866
7.311690 687798
7.524080 908475
7.615405 908463
7.948984 753608
6.803541 910931
6.2333221240202
6.868018 908472
5.548601 910900
6.404932 687799
7.906778 722058
5.958910 753600
7.2945971298348
7.4094631240185
7.990055 908476
7.2606821298350
8.770801 907786
6.959397 753592
6.980728 906805
6.845378 724859
7.646560 905949
6.6586911240146
7.316419 909721
⋮⋮
6.277130 905972
7.823917 905970
7.5016401240145
7.2214981290908
6.6563781503370
7.9911841247873
8.246154 906791
4.7516981503369
6.452249 687820
6.779306 908473
7.837235 687800
7.370621 687821
7.476429 905967
5.368187 687807
7.3822931240187
6.394580 722066
6.565660 724874
7.9944731240190
6.296948 687802
7.003254 687812
7.360163 687819
5.701877 905942
5.595287 905944
6.619834 924244
7.869154 713869
9.1386401298347
8.4862901298537
7.738085 687777
7.078411 724834
7.740654 722046
In [440]:
cor_df <- luad_Osimertinib %>% 
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% 
merge(luad_Osimertinib_exp_SLC1A5,by = 'COSMIC_ID',all = T)

head(cor_df)
A data.frame: 6 × 4
COSMIC_IDLN_IC50AUCSLC1A5
<dbl><dbl><dbl><dbl>
1687777 0.1050570.8279937.738085
2687798 0.1965950.8681517.311690
3687799-0.7015920.8008716.404932
4687800 0.4078720.8448577.837235
5687802 1.1113000.8610176.296948
6687807 3.4626170.9601855.368187
In [442]:
cor.test(cor_df$LN_IC50,cor_df$SLC1A5)
	Pearson's product-moment correlation

data:  cor_df$LN_IC50 and cor_df$SLC1A5
t = 0.77573, df = 59, p-value = 0.441
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1552687  0.3436072
sample estimates:
      cor 
0.1004808 
In [445]:
drivers$V1
  1. 'CD24'
  2. 'GTF2B'
  3. 'MSH6'
  4. 'TFDP1'
  5. 'MYO1B'
  6. 'TUBA1B'
  7. 'SLC1A5'
  8. 'TGM2'
  9. 'BARD1'
  10. 'TUBB4B'
  11. 'RRBP1'
  12. 'ANXA2'
  13. 'PLK2'
  14. 'PSMC4'
  15. 'DGKE'
  16. 'CEBPD'
  17. 'HSPA2'
  18. 'RAB31'
  19. 'TFAP2C'
  20. 'CD9'
  21. 'TPM1'
  22. 'LMO7'
  23. 'KLF5'
  24. 'TRIB1'
  25. 'HIST1H1C'
  26. 'DAAM1'
In [454]:
luad_Osimertinib_exp_m <-  luad_Osimertinib_exp%>% dplyr::filter(GENE_SYMBOLS %in% drivers$V1) %>% as.data.frame()
In [455]:
rn <- luad_Osimertinib_exp_m$GENE_SYMBOLS
luad_Osimertinib_exp_m <- luad_Osimertinib_exp_m[,2:ncol(luad_Osimertinib_exp_m)]
rownames(luad_Osimertinib_exp_m) <- rn
colnames(luad_Osimertinib_exp_m) <- substring(colnames(luad_Osimertinib_exp_m),6)
luad_Osimertinib_exp_m
A data.frame: 25 × 61
908460908465910399687816724873722045724868724866687798908475⋯68781990594290594492424471386912983471298537687777724834722046
<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
CD911.29131510.419700 7.617385 9.339332 9.48662310.159039 9.754571 9.89762511.000317 9.237512⋯10.18547710.423113 8.428533 9.664688 3.05480710.41435510.66243310.307319 9.547407 9.299587
PSMC4 7.673976 8.210058 9.738164 9.017231 9.866086 9.256839 8.236600 8.593390 9.076201 8.868797⋯ 9.592510 8.500771 8.494822 7.370401 9.345085 8.092143 8.476768 9.629509 8.267387 9.203571
TFAP2C 5.569467 6.258649 7.039517 3.848033 3.362454 7.919826 3.195641 7.507546 8.096261 3.253618⋯ 7.981333 7.793775 3.450696 5.383740 3.483883 5.962629 5.636805 3.938102 4.795427 4.777092
DAAM1 8.114960 9.206229 5.941011 7.709771 7.900853 6.872078 8.935788 6.802538 6.726673 8.064515⋯ 6.894659 7.088879 6.172727 8.090520 6.445547 7.522478 9.375249 7.165313 7.367304 7.963315
KLF5 7.832294 7.489241 7.796212 7.341222 6.370645 6.072266 6.516501 8.344255 7.477998 5.895060⋯ 7.019319 3.754168 7.242407 6.296828 3.695600 8.726176 8.450361 8.542335 5.914849 8.988621
SLC1A5 7.975696 8.479559 7.921446 8.292501 8.027455 6.631435 7.023257 6.888380 7.311690 7.524080⋯ 7.360163 5.701877 5.595287 6.619834 7.869154 9.138640 8.486290 7.738085 7.078411 7.740654
MSH6 8.630002 7.699626 8.006713 8.336342 8.004477 8.699609 7.327987 8.493030 8.865416 8.122833⋯ 8.120081 9.769026 9.210357 8.789045 9.007159 7.409149 8.381671 7.572955 8.435810 8.407667
TUBA1B11.652470 9.28050311.56741910.96800411.10588211.76247011.08540511.91763212.15439111.809011⋯11.62748912.15589311.65033311.71938812.28647511.86761710.93115011.12886512.15690011.877029
RRBP1 5.093703 5.621516 4.949829 4.740624 4.239049 4.460471 3.848750 5.200245 4.658931 4.943790⋯ 4.630746 4.579190 4.910837 5.145626 4.825844 6.193587 5.968903 3.744571 4.843388 5.645256
HSPA2 4.022142 4.67329210.379341 4.066288 5.921168 6.073845 7.51398210.919948 8.445002 7.040063⋯ 7.647243 7.42845810.218470 7.156202 5.791837 9.642464 7.133963 5.889257 5.148111 3.880645
MYO1B 7.693551 6.934147 7.422530 8.281631 6.998405 6.912161 7.326733 7.326659 6.703696 8.078448⋯ 6.951107 6.352062 5.894674 7.733862 7.608904 7.526577 7.177604 7.496097 7.186138 7.093325
LMO7 7.491591 5.415113 3.924802 5.520817 3.930904 4.342913 4.317776 5.079803 5.031783 4.513817⋯ 6.624069 4.272535 4.007376 4.386634 4.241762 7.108192 5.889982 5.100766 6.435389 6.279339
GTF2B 9.159144 9.22078210.071285 8.866781 9.923957 8.681695 8.387188 8.825132 9.251520 8.645145⋯ 8.787976 8.735390 9.410757 8.181802 9.404122 9.20459510.618004 9.514425 9.579701 8.800826
BARD1 8.495212 6.036531 5.846044 7.417529 6.664647 7.405108 6.711966 6.938701 8.135322 7.711713⋯ 7.936489 7.773346 7.973820 6.936609 8.045076 7.216246 6.439428 7.231769 7.870592 6.810523
TPM1 3.488745 5.144114 5.528843 5.493340 5.479033 6.784133 7.232806 4.886198 6.531786 5.729551⋯ 7.338494 5.081423 4.438690 4.934804 6.322667 5.110684 6.116312 7.375009 4.395875 4.419528
PLK2 3.198873 5.233384 4.314030 8.687386 7.571510 4.293385 7.589598 4.043542 7.927601 7.650513⋯ 9.155004 6.426634 4.346806 6.159305 3.512329 5.420284 7.297776 7.629353 5.117695 9.273644
DGKE 3.390216 3.243297 3.148960 3.519932 5.498992 3.095181 3.444873 3.179329 3.119441 3.204577⋯ 3.239207 3.301899 3.712990 3.515313 3.298652 3.097865 3.271151 3.005818 3.353095 2.997577
RAB31 7.970076 8.949247 9.955598 8.762328 9.385821 9.011549 9.033509 9.424291 7.169477 8.582742⋯ 6.598109 8.833973 8.376817 9.485289 4.429509 6.871682 9.847115 4.637205 8.497761 7.541860
TRIB1 5.349890 6.394922 4.632025 5.233623 4.968013 4.966839 4.751152 4.890243 5.254052 4.736097⋯ 4.490214 4.859319 4.168180 4.856539 4.728251 6.341722 6.287415 5.914905 5.852942 4.185141
ANXA2 8.757711 9.140393 8.970238 9.293531 8.209080 9.130158 9.035340 8.989978 9.314676 9.206791⋯ 9.283704 8.522241 9.119012 8.895494 6.850252 9.625852 9.081707 9.910711 9.161867 9.460952
HIST1H1C 7.706364 9.011878 7.595978 6.49680410.203426 9.261390 8.60975910.05576110.640221 9.178792⋯ 9.618030 8.405902 9.696507 8.319849 8.119829 9.576003 7.652675 7.210820 7.471382 7.422120
TUBB4B11.259558 9.92517311.16663311.46163310.18031610.93614310.81981011.55463511.72229711.514311⋯11.70873911.58169411.36381911.33994510.57054111.51656310.77257611.73913111.80022911.723652
TFDP1 6.363628 5.948515 5.209356 6.479746 5.600078 6.208124 5.127839 5.290525 7.539822 6.674423⋯ 6.781304 6.916258 5.360754 6.338752 6.107802 7.459940 6.256500 5.727986 7.008906 7.351332
TGM2 5.105010 7.035925 4.670315 4.373653 5.652386 7.282645 8.002773 3.582830 3.095883 4.511730⋯ 3.291883 4.559904 3.192086 4.859736 3.001048 6.390467 4.424562 6.663357 6.086876 6.428582
CEBPD10.79959210.320289 9.940489 9.430929 8.966978 9.323184 9.52114210.681514 8.987169 9.921542⋯ 9.619719 8.018189 9.866371 9.145795 7.174265 9.993356 7.737602 8.457290 9.924065 9.609132
In [473]:
ComplexHeatmap::Heatmap(luad_Osimertinib_exp_m)
Warning message:
“The input is a data frame-like object, convert it to a matrix.”
No description has been provided for this image
In [485]:
luad_Osimertinib_exp_TGM2 <-  luad_Osimertinib_exp%>% dplyr::filter(GENE_SYMBOLS == 'TGM2') %>% t() %>% as.data.frame()
luad_Osimertinib_exp_TGM2 <- luad_Osimertinib_exp_TGM2 %>% mutate(COSMIC_ID = substring(rownames(luad_Osimertinib_exp_TGM2),6))
luad_Osimertinib_exp_TGM2 <- luad_Osimertinib_exp_TGM2[2:nrow(luad_Osimertinib_exp_TGM2),] %>% apply(2,as.numeric)
colnames(luad_Osimertinib_exp_TGM2) <- c('TGM2','COSMIC_ID')

cor_df <- luad_Osimertinib %>% 
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% 
merge(luad_Osimertinib_exp_TGM2,by = 'COSMIC_ID',all = T)

head(cor_df)

cor.test(cor_df$LN_IC50,cor_df$TGM2)
cor.test(cor_df$AUC,cor_df$TGM2)
A data.frame: 6 × 4
COSMIC_IDLN_IC50AUCTGM2
<dbl><dbl><dbl><dbl>
1687777 0.1050570.8279936.663357
2687798 0.1965950.8681513.095883
3687799-0.7015920.8008717.434148
4687800 0.4078720.8448576.275204
5687802 1.1113000.8610176.313773
6687807 3.4626170.9601853.469466
	Pearson's product-moment correlation

data:  cor_df$LN_IC50 and cor_df$TGM2
t = -0.11523, df = 59, p-value = 0.9087
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2658161  0.2377193
sample estimates:
        cor 
-0.01499935 
	Pearson's product-moment correlation

data:  cor_df$AUC and cor_df$TGM2
t = -0.4178, df = 59, p-value = 0.6776
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3020029  0.2002469
sample estimates:
        cor 
-0.05431266 
In [481]:
dri10 <- drivers %>% dplyr::filter(counts == 10)
In [482]:
luad_Osimertinib_exp_m <-  luad_Osimertinib_exp%>% dplyr::filter(GENE_SYMBOLS %in% dri10$V1) %>% as.data.frame()
In [483]:
rn <- luad_Osimertinib_exp_m$GENE_SYMBOLS
luad_Osimertinib_exp_m <- luad_Osimertinib_exp_m[,2:ncol(luad_Osimertinib_exp_m)]
rownames(luad_Osimertinib_exp_m) <- rn
colnames(luad_Osimertinib_exp_m) <- substring(colnames(luad_Osimertinib_exp_m),6)
luad_Osimertinib_exp_m
A data.frame: 9 × 61
908460908465910399687816724873722045724868724866687798908475⋯68781990594290594492424471386912983471298537687777724834722046
<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
SLC1A5 7.9756968.479559 7.921446 8.292501 8.027455 6.631435 7.023257 6.888380 7.311690 7.524080⋯ 7.360163 5.701877 5.595287 6.619834 7.869154 9.138640 8.486290 7.738085 7.078411 7.740654
MSH6 8.6300027.699626 8.006713 8.336342 8.004477 8.699609 7.327987 8.493030 8.865416 8.122833⋯ 8.120081 9.769026 9.210357 8.789045 9.007159 7.409149 8.381671 7.572955 8.435810 8.407667
TUBA1B11.6524709.28050311.56741910.96800411.10588211.76247011.08540511.91763212.15439111.809011⋯11.62748912.15589311.65033311.71938812.28647511.86761710.93115011.12886512.15690011.877029
MYO1B 7.6935516.934147 7.422530 8.281631 6.998405 6.912161 7.326733 7.326659 6.703696 8.078448⋯ 6.951107 6.352062 5.894674 7.733862 7.608904 7.526577 7.177604 7.496097 7.186138 7.093325
GTF2B 9.1591449.22078210.071285 8.866781 9.923957 8.681695 8.387188 8.825132 9.251520 8.645145⋯ 8.787976 8.735390 9.410757 8.181802 9.404122 9.20459510.618004 9.514425 9.579701 8.800826
BARD1 8.4952126.036531 5.846044 7.417529 6.664647 7.405108 6.711966 6.938701 8.135322 7.711713⋯ 7.936489 7.773346 7.973820 6.936609 8.045076 7.216246 6.439428 7.231769 7.870592 6.810523
TUBB4B11.2595589.92517311.16663311.46163310.18031610.93614310.81981011.55463511.72229711.514311⋯11.70873911.58169411.36381911.33994510.57054111.51656310.77257611.73913111.80022911.723652
TFDP1 6.3636285.948515 5.209356 6.479746 5.600078 6.208124 5.127839 5.290525 7.539822 6.674423⋯ 6.781304 6.916258 5.360754 6.338752 6.107802 7.459940 6.256500 5.727986 7.008906 7.351332
TGM2 5.1050107.035925 4.670315 4.373653 5.652386 7.282645 8.002773 3.582830 3.095883 4.511730⋯ 3.291883 4.559904 3.192086 4.859736 3.001048 6.390467 4.424562 6.663357 6.086876 6.428582
In [484]:
ComplexHeatmap::Heatmap(luad_Osimertinib_exp_m)
Warning message:
“The input is a data frame-like object, convert it to a matrix.”
No description has been provided for this image
In [486]:
Osimertinib <- GDSC_drug2 %>% dplyr::filter(GDSC_drug2$COSMIC_ID %in% substring(colnames(GDSC_exp),6)) %>%
    dplyr::filter(DRUG_NAME == 'Osimertinib') 

Osimertinib
A data.frame: 929 × 19
DATASETNLME_RESULT_IDNLME_CURVE_IDCOSMIC_IDCELL_LINE_NAMESANGER_MODEL_IDTCGA_DESCDRUG_IDDRUG_NAMEPUTATIVE_TARGETPATHWAY_NAMECOMPANY_IDWEBRELEASEMIN_CONCMAX_CONCLN_IC50AUCRMSEZ_SCORE
<chr><dbl><dbl><dbl><chr><chr><chr><dbl><chr><chr><chr><dbl><chr><dbl><dbl><dbl><dbl><dbl><dbl>
GDSC234315946501683667PFSK-1 SIDM01132MB 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.2798760.9488510.083039-0.290963
GDSC234315946766684052A673 SIDM00848UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.3725550.9554870.075005-0.218362
GDSC234315947052684057ES5 SIDM00263UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.1678340.9477250.057817-0.378733
GDSC234315947305684059ES7 SIDM00269UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8651740.9699780.024503 0.167538
GDSC234315947587684062EW-11 SIDM00203UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.3350560.9349420.039643-0.247737
GDSC234315947867684072SK-ES-1 SIDM01111UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.6580290.9727900.076845 0.005268
GDSC234315948147687448COLO-829 SIDM00909SKCM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.9189380.9382080.056445 0.993017
GDSC2343159484266874525637 SIDM00807BLCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.4574850.8889550.070200-0.935194
GDSC234315948707687455RT4 SIDM01085BLCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.7845370.9814130.067026 0.887732
GDSC234315948988687457SW780 SIDM01160BLCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.3017370.9589790.058973 0.509525
GDSC234315949269687459TCCSUP SIDM01190BLCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.7562580.9652900.044146 0.865579
GDSC234315949552687505C-33-A SIDM00889CESC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.1892400.8834800.063777-1.145327
GDSC234315949831687506C-4-I SIDM00905CESC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.2765880.9064730.040211-1.076901
GDSC234315950113687514ME-180 SIDM00627CESC 1919OsimertinibEGFREGFR signaling1046Y0.0010011-0.0374130.8603570.026376-1.322878
GDSC23431595039368756142-MG-BA SIDM00982GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.4752810.9493910.042978-0.137890
GDSC2343159506736875628-MG-BA SIDM00998GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.3680880.9824570.082000 0.561502
GDSC234315950954687563A172 SIDM00799GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.5880130.9423000.081864-0.049580
GDSC234315951235687568GB-1 SIDM00581GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.9525660.9755610.104329 1.802722
GDSC234315951518687586T98G SIDM01171GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.8691080.9812810.019718 0.953982
GDSC234315951798687588U-118-MG SIDM01193GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.7960250.9267950.059708 0.113368
GDSC234315952059687590U-87-MG SIDM01189GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.3044010.9588070.034969 0.511612
GDSC234315952260687592YKG-1 SIDM00315GBM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.6411810.9461040.080434-0.007930
GDSC234315952440687596ChaGo-K-1SIDM00924UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.0416740.9697240.126241 0.305801
GDSC234315952683687600NCI-H720 SIDM01120UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.2716990.9760710.116991 0.485994
GDSC234315952962687777Calu-3 SIDM00922LUAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.1050570.8279930.098212-1.211272
GDSC234315953242687780COR-L23 SIDM00512UNCLASSIFIED1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.2726150.9542360.060843 0.486711
GDSC234315953524687787LK-2 SIDM00548LUSC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.4409810.9352440.065657-0.164759
GDSC234315953984687798NCI-H1623SIDM00747LUAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.1965950.8681510.052729-1.139565
GDSC234315954155687799NCI-H1648SIDM00746LUAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011-0.7015920.8008710.093298-1.843171
GDSC234315954434687800NCI-H1650SIDM00745LUAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.4078720.8448570.134400-0.974058
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
GDSC2343161821441480372PEO1 SIDM00472OV 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.7509450.9497250.032682 0.861418
GDSC2343161823531480374UWB1.289 SIDM00815OV 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.2369740.8630540.068164-1.107934
GDSC2343161826061503361FLO-1 SIDM01041ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8595030.9458650.059545 0.163096
GDSC2343161828861503362OACp4C SIDM00445ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8227520.9565460.075153 0.134306
GDSC2343161831671503363OACM5-1 SIDM00444ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.2115940.9745480.028010 1.222273
GDSC2343161834481503364SK-GT-2 SIDM00393STAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011-3.0669200.5484530.117406-3.696080
GDSC2343161837301503365SK-GT-4 SIDM00483ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8634690.8827330.073594 0.166202
GDSC2343161840091503366ESO26 SIDM00539ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.2391660.9343990.042767-0.322854
GDSC2343161842901503367ESO51 SIDM00538ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 3.1579520.9754120.024334 1.180251
GDSC2343161845731503368KYAE-1 SIDM00530ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011-1.5601720.7398310.039288-2.515751
GDSC2343161847821503369EMC-BAC-1SIDM00048LUAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011-0.0425160.8568870.132649-1.326876
GDSC2343161850321503370EMC-BAC-2SIDM00047LUAD 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.6643140.8863200.090450-0.773171
GDSC2343161853131503371TE-4 SIDM00250ESCA 1919OsimertinibEGFREGFR signaling1046Y0.0010011-1.7073180.7239210.058785-2.631019
GDSC2343161857791509073NCC010 SIDM00231KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.5747130.9217360.083888-0.843361
GDSC2343161859591509074NCC021 SIDM00232KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.3387820.9538480.078305 0.538544
GDSC2343161861391524414RCC-FG2 SIDM00819KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.8465490.9116540.134748-0.630415
GDSC2343161863191524415RCC-JF SIDM00818KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.9267480.9236100.066450-0.567591
GDSC2343161865711524416RCC-JW SIDM00817KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.2371970.9541330.037277 0.458967
GDSC2343161867801524417RCC-ER SIDM00820KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.6808200.9566400.019253 0.023121
GDSC2343161869601524418RCC-AB SIDM00821KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.8564230.8885320.088464-0.622681
GDSC2343161871401524419RCC-MF SIDM00816KIRC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.9654170.9647310.062086 0.246064
GDSC2343161873201659817KMS-11 SIDM00608MM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.5764690.9672250.059140-0.058623
GDSC2343161875701659818MM1S SIDM01265MM 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.8620970.9678790.008336-0.618235
GDSC2343161877761659819OCI-LY7 SIDM00459DLBC 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.9484920.9753690.019785-0.550557
GDSC2343161881781659928SNU-175 SIDM00216COREAD1919OsimertinibEGFREGFR signaling1046Y0.0010011-0.2119490.8579020.056394-1.459603
GDSC2343161883881659929SNU-283 SIDM00215NA 1919OsimertinibEGFREGFR signaling1046Y0.0010011 0.6195650.9130370.081642-0.808226
GDSC2343161886401660034SNU-407 SIDM00214COREAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.1157100.9199570.050110-0.419564
GDSC2343161889101660035SNU-61 SIDM00194COREAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.3789730.9644790.044681 0.570028
GDSC2343161891771660036SNU-81 SIDM00193COREAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 1.8912710.9757640.057990 0.187981
GDSC2343161894291674021SNU-C5 SIDM00498COREAD1919OsimertinibEGFREGFR signaling1046Y0.0010011 2.5035270.9576380.057810 0.667600
In [681]:
ComplexHeatmap::Heatmap(normalize(luad_Osimertinib[,c('LN_IC50')]))
No description has been provided for this image
In [683]:
ComplexHeatmap::Heatmap(normalize(Osimertinib[,c('LN_IC50')]))
No description has been provided for this image
In [682]:
ComplexHeatmap::Heatmap(normalize(luad_Osimertinib[,c('AUC')]))
No description has been provided for this image
In [684]:
ComplexHeatmap::Heatmap(normalize(Osimertinib[,c('AUC')]))
No description has been provided for this image
In [ ]:
用权重加权基因算得分 只看正 只看负 加载一起
In [ ]:
和药物反应相关高的基因与driver找关联
In [ ]:
提高driver范围,在所以癌症中做
In [ ]:
跨组学去找CNV
In [ ]:

In [17]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_sen_res'
read_dir <- file.path(read_dir,run_name)
In [18]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
A data.table: 6 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
CD24 0.00083927350.0003266406-5.656427e-0510FALSEFALSEFALSEFALSE
GTF2B 0.00103246970.0003080479-1.316672e-0410 TRUEFALSEFALSEFALSE
MSH6 0.00166129930.0004270973 7.425508e-0510FALSEFALSEFALSEFALSE
TFDP1 0.00137155780.0005694479-5.785423e-0510 TRUEFALSEFALSEFALSE
MYO1B 0.00101956440.0002886838 1.748662e-0410FALSEFALSEFALSEFALSE
TUBA1B0.00183920790.0003183509-1.412048e-0410FALSEFALSEFALSEFALSE
In [19]:
drivers
A data.table: 26 × 9
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
CD24 0.00083927353.266406e-04-5.656427e-0510FALSEFALSEFALSEFALSE
GTF2B 0.00103246973.080479e-04-1.316672e-0410 TRUEFALSEFALSEFALSE
MSH6 0.00166129934.270973e-04 7.425508e-0510FALSEFALSEFALSEFALSE
TFDP1 0.00137155785.694479e-04-5.785423e-0510 TRUEFALSEFALSEFALSE
MYO1B 0.00101956442.886838e-04 1.748662e-0410FALSEFALSEFALSEFALSE
TUBA1B 0.00183920793.183509e-04-1.412048e-0410FALSEFALSEFALSEFALSE
SLC1A5 0.00219760486.430204e-04 1.082030e-0410FALSEFALSEFALSEFALSE
TGM2 0.00074156612.308803e-04 5.989809e-0610FALSEFALSEFALSEFALSE
BARD1 0.00216733606.107930e-04 1.084314e-0410FALSEFALSEFALSEFALSE
TUBB4B 0.00063758482.468592e-04 1.148352e-0510FALSEFALSEFALSEFALSE
RRBP1 0.00068093032.972012e-04-1.221697e-05 9FALSEFALSEFALSEFALSE
ANXA2 0.00134275096.148865e-04-7.150776e-05 9FALSEFALSEFALSEFALSE
PLK2 0.00113224025.536364e-04 6.720297e-05 9FALSEFALSEFALSEFALSE
PSMC4 0.00092144433.828258e-04-4.451585e-05 9FALSEFALSEFALSEFALSE
DGKE 0.00097306222.876285e-04-5.831443e-05 9FALSEFALSEFALSEFALSE
CEBPD 0.00061215972.758779e-04 7.729752e-05 9 TRUEFALSEFALSEFALSE
HSPA2 0.00069958642.921626e-04-2.506919e-04 8FALSEFALSEFALSEFALSE
RAB31 0.00114500013.613637e-04-5.959134e-05 8FALSEFALSEFALSEFALSE
TFAP2C 0.00024000258.345076e-05 1.130995e-05 7 TRUEFALSEFALSEFALSE
CD9 0.00208977184.177533e-04-1.463574e-04 6FALSEFALSEFALSEFALSE
TPM1 0.00033375731.718587e-04 2.991494e-05 6FALSEFALSEFALSEFALSE
LMO7 0.00060439602.033280e-04 2.352714e-04 6FALSEFALSEFALSEFALSE
KLF5 0.00036248022.617805e-04 1.071805e-04 6 TRUEFALSEFALSEFALSE
TRIB1 0.00047530062.120645e-04 2.118026e-05 6FALSEFALSEFALSEFALSE
HIST1H1C0.00038036971.031536e-04-7.409843e-05 4FALSEFALSEFALSEFALSE
DAAM1 0.00207008054.885557e-04 1.041791e-04 4FALSEFALSEFALSEFALSE
In [20]:
luad_Osimertinib_exp_m <-  luad_Osimertinib_exp%>% dplyr::filter(GENE_SYMBOLS %in% drivers$V1) %>% as.data.frame()
In [21]:
rn <- luad_Osimertinib_exp_m$GENE_SYMBOLS
luad_Osimertinib_exp_m <- luad_Osimertinib_exp_m[,2:ncol(luad_Osimertinib_exp_m)]
rownames(luad_Osimertinib_exp_m) <- rn
colnames(luad_Osimertinib_exp_m) <- substring(colnames(luad_Osimertinib_exp_m),6)
luad_Osimertinib_exp_m
A data.frame: 25 × 61
908460908465910399687816724873722045724868724866687798908475⋯68781990594290594492424471386912983471298537687777724834722046
<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
CD911.29131510.419700 7.617385 9.339332 9.48662310.159039 9.754571 9.89762511.000317 9.237512⋯10.18547710.423113 8.428533 9.664688 3.05480710.41435510.66243310.307319 9.547407 9.299587
PSMC4 7.673976 8.210058 9.738164 9.017231 9.866086 9.256839 8.236600 8.593390 9.076201 8.868797⋯ 9.592510 8.500771 8.494822 7.370401 9.345085 8.092143 8.476768 9.629509 8.267387 9.203571
TFAP2C 5.569467 6.258649 7.039517 3.848033 3.362454 7.919826 3.195641 7.507546 8.096261 3.253618⋯ 7.981333 7.793775 3.450696 5.383740 3.483883 5.962629 5.636805 3.938102 4.795427 4.777092
DAAM1 8.114960 9.206229 5.941011 7.709771 7.900853 6.872078 8.935788 6.802538 6.726673 8.064515⋯ 6.894659 7.088879 6.172727 8.090520 6.445547 7.522478 9.375249 7.165313 7.367304 7.963315
KLF5 7.832294 7.489241 7.796212 7.341222 6.370645 6.072266 6.516501 8.344255 7.477998 5.895060⋯ 7.019319 3.754168 7.242407 6.296828 3.695600 8.726176 8.450361 8.542335 5.914849 8.988621
SLC1A5 7.975696 8.479559 7.921446 8.292501 8.027455 6.631435 7.023257 6.888380 7.311690 7.524080⋯ 7.360163 5.701877 5.595287 6.619834 7.869154 9.138640 8.486290 7.738085 7.078411 7.740654
MSH6 8.630002 7.699626 8.006713 8.336342 8.004477 8.699609 7.327987 8.493030 8.865416 8.122833⋯ 8.120081 9.769026 9.210357 8.789045 9.007159 7.409149 8.381671 7.572955 8.435810 8.407667
TUBA1B11.652470 9.28050311.56741910.96800411.10588211.76247011.08540511.91763212.15439111.809011⋯11.62748912.15589311.65033311.71938812.28647511.86761710.93115011.12886512.15690011.877029
RRBP1 5.093703 5.621516 4.949829 4.740624 4.239049 4.460471 3.848750 5.200245 4.658931 4.943790⋯ 4.630746 4.579190 4.910837 5.145626 4.825844 6.193587 5.968903 3.744571 4.843388 5.645256
HSPA2 4.022142 4.67329210.379341 4.066288 5.921168 6.073845 7.51398210.919948 8.445002 7.040063⋯ 7.647243 7.42845810.218470 7.156202 5.791837 9.642464 7.133963 5.889257 5.148111 3.880645
MYO1B 7.693551 6.934147 7.422530 8.281631 6.998405 6.912161 7.326733 7.326659 6.703696 8.078448⋯ 6.951107 6.352062 5.894674 7.733862 7.608904 7.526577 7.177604 7.496097 7.186138 7.093325
LMO7 7.491591 5.415113 3.924802 5.520817 3.930904 4.342913 4.317776 5.079803 5.031783 4.513817⋯ 6.624069 4.272535 4.007376 4.386634 4.241762 7.108192 5.889982 5.100766 6.435389 6.279339
GTF2B 9.159144 9.22078210.071285 8.866781 9.923957 8.681695 8.387188 8.825132 9.251520 8.645145⋯ 8.787976 8.735390 9.410757 8.181802 9.404122 9.20459510.618004 9.514425 9.579701 8.800826
BARD1 8.495212 6.036531 5.846044 7.417529 6.664647 7.405108 6.711966 6.938701 8.135322 7.711713⋯ 7.936489 7.773346 7.973820 6.936609 8.045076 7.216246 6.439428 7.231769 7.870592 6.810523
TPM1 3.488745 5.144114 5.528843 5.493340 5.479033 6.784133 7.232806 4.886198 6.531786 5.729551⋯ 7.338494 5.081423 4.438690 4.934804 6.322667 5.110684 6.116312 7.375009 4.395875 4.419528
PLK2 3.198873 5.233384 4.314030 8.687386 7.571510 4.293385 7.589598 4.043542 7.927601 7.650513⋯ 9.155004 6.426634 4.346806 6.159305 3.512329 5.420284 7.297776 7.629353 5.117695 9.273644
DGKE 3.390216 3.243297 3.148960 3.519932 5.498992 3.095181 3.444873 3.179329 3.119441 3.204577⋯ 3.239207 3.301899 3.712990 3.515313 3.298652 3.097865 3.271151 3.005818 3.353095 2.997577
RAB31 7.970076 8.949247 9.955598 8.762328 9.385821 9.011549 9.033509 9.424291 7.169477 8.582742⋯ 6.598109 8.833973 8.376817 9.485289 4.429509 6.871682 9.847115 4.637205 8.497761 7.541860
TRIB1 5.349890 6.394922 4.632025 5.233623 4.968013 4.966839 4.751152 4.890243 5.254052 4.736097⋯ 4.490214 4.859319 4.168180 4.856539 4.728251 6.341722 6.287415 5.914905 5.852942 4.185141
ANXA2 8.757711 9.140393 8.970238 9.293531 8.209080 9.130158 9.035340 8.989978 9.314676 9.206791⋯ 9.283704 8.522241 9.119012 8.895494 6.850252 9.625852 9.081707 9.910711 9.161867 9.460952
HIST1H1C 7.706364 9.011878 7.595978 6.49680410.203426 9.261390 8.60975910.05576110.640221 9.178792⋯ 9.618030 8.405902 9.696507 8.319849 8.119829 9.576003 7.652675 7.210820 7.471382 7.422120
TUBB4B11.259558 9.92517311.16663311.46163310.18031610.93614310.81981011.55463511.72229711.514311⋯11.70873911.58169411.36381911.33994510.57054111.51656310.77257611.73913111.80022911.723652
TFDP1 6.363628 5.948515 5.209356 6.479746 5.600078 6.208124 5.127839 5.290525 7.539822 6.674423⋯ 6.781304 6.916258 5.360754 6.338752 6.107802 7.459940 6.256500 5.727986 7.008906 7.351332
TGM2 5.105010 7.035925 4.670315 4.373653 5.652386 7.282645 8.002773 3.582830 3.095883 4.511730⋯ 3.291883 4.559904 3.192086 4.859736 3.001048 6.390467 4.424562 6.663357 6.086876 6.428582
CEBPD10.79959210.320289 9.940489 9.430929 8.966978 9.323184 9.52114210.681514 8.987169 9.921542⋯ 9.619719 8.018189 9.866371 9.145795 7.174265 9.993356 7.737602 8.457290 9.924065 9.609132
In [22]:
luad_Osimertinib_exp_m$V1 = rownames(luad_Osimertinib_exp_m)
In [23]:
score_df <- merge(luad_Osimertinib_exp_m,drivers,by = 'V1',all = F)
score_df
A data.frame: 25 × 70
V1908460908465910399687816724873722045724868724866687798⋯724834722046weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathway
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl>
ANXA2 8.757711 9.140393 8.970238 9.293531 8.209080 9.130158 9.035340 8.989978 9.314676⋯ 9.161867 9.4609520.00134275096.148865e-04-7.150776e-05 9FALSEFALSEFALSEFALSE
BARD1 8.495212 6.036531 5.846044 7.417529 6.664647 7.405108 6.711966 6.938701 8.135322⋯ 7.870592 6.8105230.00216733606.107930e-04 1.084314e-0410FALSEFALSEFALSEFALSE
CD9 11.29131510.419700 7.617385 9.339332 9.48662310.159039 9.754571 9.89762511.000317⋯ 9.547407 9.2995870.00208977184.177533e-04-1.463574e-04 6FALSEFALSEFALSEFALSE
CEBPD 10.79959210.320289 9.940489 9.430929 8.966978 9.323184 9.52114210.681514 8.987169⋯ 9.924065 9.6091320.00061215972.758779e-04 7.729752e-05 9 TRUEFALSEFALSEFALSE
DAAM1 8.114960 9.206229 5.941011 7.709771 7.900853 6.872078 8.935788 6.802538 6.726673⋯ 7.367304 7.9633150.00207008054.885557e-04 1.041791e-04 4FALSEFALSEFALSEFALSE
DGKE 3.390216 3.243297 3.148960 3.519932 5.498992 3.095181 3.444873 3.179329 3.119441⋯ 3.353095 2.9975770.00097306222.876285e-04-5.831443e-05 9FALSEFALSEFALSEFALSE
GTF2B 9.159144 9.22078210.071285 8.866781 9.923957 8.681695 8.387188 8.825132 9.251520⋯ 9.579701 8.8008260.00103246973.080479e-04-1.316672e-0410 TRUEFALSEFALSEFALSE
HIST1H1C 7.706364 9.011878 7.595978 6.49680410.203426 9.261390 8.60975910.05576110.640221⋯ 7.471382 7.4221200.00038036971.031536e-04-7.409843e-05 4FALSEFALSEFALSEFALSE
HSPA2 4.022142 4.67329210.379341 4.066288 5.921168 6.073845 7.51398210.919948 8.445002⋯ 5.148111 3.8806450.00069958642.921626e-04-2.506919e-04 8FALSEFALSEFALSEFALSE
KLF5 7.832294 7.489241 7.796212 7.341222 6.370645 6.072266 6.516501 8.344255 7.477998⋯ 5.914849 8.9886210.00036248022.617805e-04 1.071805e-04 6 TRUEFALSEFALSEFALSE
LMO7 7.491591 5.415113 3.924802 5.520817 3.930904 4.342913 4.317776 5.079803 5.031783⋯ 6.435389 6.2793390.00060439602.033280e-04 2.352714e-04 6FALSEFALSEFALSEFALSE
MSH6 8.630002 7.699626 8.006713 8.336342 8.004477 8.699609 7.327987 8.493030 8.865416⋯ 8.435810 8.4076670.00166129934.270973e-04 7.425508e-0510FALSEFALSEFALSEFALSE
MYO1B 7.693551 6.934147 7.422530 8.281631 6.998405 6.912161 7.326733 7.326659 6.703696⋯ 7.186138 7.0933250.00101956442.886838e-04 1.748662e-0410FALSEFALSEFALSEFALSE
PLK2 3.198873 5.233384 4.314030 8.687386 7.571510 4.293385 7.589598 4.043542 7.927601⋯ 5.117695 9.2736440.00113224025.536364e-04 6.720297e-05 9FALSEFALSEFALSEFALSE
PSMC4 7.673976 8.210058 9.738164 9.017231 9.866086 9.256839 8.236600 8.593390 9.076201⋯ 8.267387 9.2035710.00092144433.828258e-04-4.451585e-05 9FALSEFALSEFALSEFALSE
RAB31 7.970076 8.949247 9.955598 8.762328 9.385821 9.011549 9.033509 9.424291 7.169477⋯ 8.497761 7.5418600.00114500013.613637e-04-5.959134e-05 8FALSEFALSEFALSEFALSE
RRBP1 5.093703 5.621516 4.949829 4.740624 4.239049 4.460471 3.848750 5.200245 4.658931⋯ 4.843388 5.6452560.00068093032.972012e-04-1.221697e-05 9FALSEFALSEFALSEFALSE
SLC1A5 7.975696 8.479559 7.921446 8.292501 8.027455 6.631435 7.023257 6.888380 7.311690⋯ 7.078411 7.7406540.00219760486.430204e-04 1.082030e-0410FALSEFALSEFALSEFALSE
TFAP2C 5.569467 6.258649 7.039517 3.848033 3.362454 7.919826 3.195641 7.507546 8.096261⋯ 4.795427 4.7770920.00024000258.345076e-05 1.130995e-05 7 TRUEFALSEFALSEFALSE
TFDP1 6.363628 5.948515 5.209356 6.479746 5.600078 6.208124 5.127839 5.290525 7.539822⋯ 7.008906 7.3513320.00137155785.694479e-04-5.785423e-0510 TRUEFALSEFALSEFALSE
TGM2 5.105010 7.035925 4.670315 4.373653 5.652386 7.282645 8.002773 3.582830 3.095883⋯ 6.086876 6.4285820.00074156612.308803e-04 5.989809e-0610FALSEFALSEFALSEFALSE
TPM1 3.488745 5.144114 5.528843 5.493340 5.479033 6.784133 7.232806 4.886198 6.531786⋯ 4.395875 4.4195280.00033375731.718587e-04 2.991494e-05 6FALSEFALSEFALSEFALSE
TRIB1 5.349890 6.394922 4.632025 5.233623 4.968013 4.966839 4.751152 4.890243 5.254052⋯ 5.852942 4.1851410.00047530062.120645e-04 2.118026e-05 6FALSEFALSEFALSEFALSE
TUBA1B 11.652470 9.28050311.56741910.96800411.10588211.76247011.08540511.91763212.154391⋯12.15690011.8770290.00183920793.183509e-04-1.412048e-0410FALSEFALSEFALSEFALSE
TUBB4B 11.259558 9.92517311.16663311.46163310.18031610.93614310.81981011.55463511.722297⋯11.80022911.7236520.00063758482.468592e-04 1.148352e-0510FALSEFALSEFALSEFALSE
In [31]:
FUN1 <- function(x){
    sum(x*score_df$weight_shap_total_mean)
    #sum(x)
}
score <- apply(score_df[,2:61],MARGIN  = 2,FUN = FUN1)
In [32]:
score
908460
0.212208495548303
908465
0.206960413533671
910399
0.199472418370317
687816
0.210476703599609
724873
0.208599987840974
722045
0.205912328570108
724868
0.206824422476044
724866
0.206680734703705
687798
0.216285826784939
908475
0.211272650817056
908463
0.212709167064642
753608
0.214862714858326
910931
0.208238535189379
1240202
0.221460628591852
908472
0.199364266689227
910900
0.206122945311186
687799
0.207275870549291
722058
0.211096427406128
753600
0.196932229169657
1298348
0.220622285821822
1240185
0.205585230366729
908476
0.208683186256861
1298350
0.214824656364828
907786
0.213957161925956
753592
0.208851848262052
906805
0.206267698997767
724859
0.199211692897873
905949
0.205975609580769
1240146
0.201502450359524
909721
0.209140034748241
724878
0.200425672922387
905972
0.197447093167736
905970
0.214730379524127
1240145
0.208943355941955
1290908
0.212126278350503
1503370
0.205649917002817
1247873
0.212802015899291
906791
0.213492024817614
1503369
0.215298916088862
687820
0.207231800649215
908473
0.213490138123903
687800
0.223231513028422
687821
0.206601909617251
905967
0.209417767289827
687807
0.203234420099537
1240187
0.202968045468593
722066
0.19802808950732
724874
0.213749514385265
1240190
0.205352198153527
687802
0.210866998533979
687812
0.206712523847301
687819
0.212651066975101
905942
0.206288622478281
905944
0.197146764420502
924244
0.205988603788793
713869
0.180809653345578
1298347
0.218419961891229
1298537
0.217770564747933
687777
0.205889757940065
724834
0.209015402840839
In [36]:
drug_res <- luad_Osimertinib %>% 
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% filter(COSMIC_ID %in% names(score))
head(drug_res)
A data.frame: 6 × 3
COSMIC_IDLN_IC50AUC
<dbl><dbl><dbl>
1687777 0.1050570.827993
2687798 0.1965950.868151
3687799-0.7015920.800871
4687800 0.4078720.844857
5687802 1.1113000.861017
6687807 3.4626170.960185
In [40]:
drug_res$score = score[as.character(drug_res$COSMIC_ID)]
In [47]:
#cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method =  'pearson')
#cor.test(drug_res$LN_IC50,drug_res$score,method =  'kendall')
cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method =  'spearman')
cor.test(drug_res$AUC,drug_res$score,alternative = 'less',method =  'spearman')
	Spearman's rank correlation rho

data:  drug_res$LN_IC50 and drug_res$score
S = 45740, p-value = 0.01831
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.2709086 
	Spearman's rank correlation rho

data:  drug_res$AUC and drug_res$score
S = 45068, p-value = 0.02606
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.2522367 
In [43]:
?cor.test
In [53]:
normalize <- function(v) {
  (v - min(v)) / (max(v) - min(v))
}
FUN2 <- function(x){
    sum(x*normalize(score_df$weight_shap_total_mean))
    #sum(x)
}
FUN3 <- function(x){
    mean(x*normalize(score_df$weight_shap_total_mean))
    #sum(x)
}
score <- apply(score_df[,2:61],MARGIN  = 2,FUN = FUN2)
In [54]:
drug_res$score2 = score[as.character(drug_res$COSMIC_ID)]
In [55]:
cor.test(drug_res$LN_IC50,drug_res$score2,alternative = 'less',method =  'spearman')
cor.test(drug_res$AUC,drug_res$score2,alternative = 'less',method =  'spearman')
	Spearman's rank correlation rho

data:  drug_res$LN_IC50 and drug_res$score2
S = 45936, p-value = 0.01645
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.2763545 
	Spearman's rank correlation rho

data:  drug_res$AUC and drug_res$score2
S = 44496, p-value = 0.03461
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.2363434 
In [60]:
FUN3 <- function(x){
    mean(x*normalize(score_df$weight_shap_total_mean))
    #sum(x)
}
score <- apply(score_df[,2:61],MARGIN  = 2,FUN = FUN3)

drug_res$score3 = score[as.character(drug_res$COSMIC_ID)]
In [63]:
cor.test(drug_res$LN_IC50,drug_res$score3,alternative = 'less',method =  'spearman')
cor.test(drug_res$AUC,drug_res$score3,alternative = 'less',method =  'spearman')
	Spearman's rank correlation rho

data:  drug_res$LN_IC50 and drug_res$score3
S = 45936, p-value = 0.01645
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.2763545 
	Spearman's rank correlation rho

data:  drug_res$AUC and drug_res$score3
S = 44496, p-value = 0.03461
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.2363434 
In [62]:
drug_res
A data.frame: 60 × 6
COSMIC_IDLN_IC50AUCscorescore2score3
<dbl><dbl><dbl><dbl><dbl><dbl>
687777 0.1050570.8279930.205889882.705393.308216
687798 0.1965950.8681510.216285886.671493.466860
687799-0.7015920.8008710.207275983.413273.336531
687800 0.4078720.8448570.223231589.303613.572144
687802 1.1113000.8610170.210867084.533763.381350
687807 3.4626170.9601850.203234482.191643.287666
687812 1.7737920.9392890.206712582.564413.302576
687816 3.0722260.9545290.210476785.084333.403373
687819 2.6489100.9666160.212651185.029863.401194
687820 3.5539660.9464250.207231882.897183.315887
687821 3.3331930.9304230.206601982.632073.305283
713869 2.7889540.9680420.180809772.842352.913694
722045 0.6756240.8817350.205912382.438433.297537
722058 1.8732610.9800540.211096485.169903.406796
722066 2.8744540.9856840.198028180.191373.207655
724834 3.4114230.9709480.209015484.298813.371952
724859 3.4815900.9683560.199211779.717923.188717
724866 3.2281940.9568010.206680782.368603.294744
724868 2.1935700.9781270.206824483.173063.326923
724873 2.3737730.9616200.208600084.059543.362382
724874 1.8106790.8981880.213749586.514623.460585
724878 4.6099790.9779020.200425780.819913.232796
753592 2.3434460.9752250.208851883.856783.354271
753600 0.8829320.9170990.196932278.226613.129064
753608-3.3043650.5180410.214862786.826203.473048
905942 2.9424270.9880780.206288683.207683.328307
905944 1.8520380.9609710.197146879.093473.163739
905949 1.2129450.9196100.205975681.963293.278532
905967-1.3807720.7231520.209417883.778893.351156
905970 1.1649360.8751310.214730485.665643.426626
905972 2.7421710.9559520.197447178.959323.158373
906791 2.8415050.9695010.213492085.946743.437869
906805-1.3827190.7074020.206267782.406783.296271
907786 1.6393990.9641060.213957286.378813.455152
908460 5.4599080.9658040.212208585.833363.433334
908463 3.7020490.9549300.212709285.784083.431363
908465 0.6459060.8600030.206960483.004523.320181
908472 3.1689000.9827030.199364379.808223.192329
908473 0.4475610.8344630.213490185.192673.407707
908475 1.8909450.9400010.211272785.289803.411592
908476 2.0916010.9521260.208683283.136673.325467
909721 4.5600970.9697730.209140083.673143.346926
910399 1.6369130.9541370.199472479.417033.176681
910900 1.7809430.9617970.206122982.629253.305170
910931 1.9832890.9686380.208238583.496483.339859
924244-3.2721240.5212020.205988682.994343.319773
1240145 2.4733960.9707960.208943484.398523.375941
1240146-3.5907240.4854250.201502581.325933.253037
1240185 1.0887070.9000650.205585282.659033.306361
1240187 3.0620890.9657890.202968081.164293.246572
1240190 2.1510880.9490500.205352282.632143.305285
1240202-2.6045360.5918820.221460689.115813.564632
1247873-2.5233540.5940910.212802086.157233.446289
1290908 0.9634380.9012440.212126385.797433.431897
1298347 2.2154200.9884400.218420087.370373.494815
1298348 0.4691930.8550170.220622388.595303.543812
1298350-0.3484100.8242950.214824786.472633.458905
1298537 1.4152620.9460080.217770687.658423.506337
1503369-0.0425160.8568870.215298986.486333.459453
1503370 0.6643140.8863200.205649981.945533.277821
In [65]:
drivers$directions = drivers$weight_grad_total_dir_mean >= 0
In [73]:
drivers_pos = drivers %>% filter(drivers$directions)
drivers_neg = drivers %>% filter(!drivers$directions)
In [75]:
score_df <- merge(luad_Osimertinib_exp_m,drivers_pos,by = 'V1',all = F)
score <- apply(score_df[,2:61],MARGIN  = 2,FUN = FUN1)

drug_res <- luad_Osimertinib %>% 
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% filter(COSMIC_ID %in% names(score))
drug_res$score = score[as.character(drug_res$COSMIC_ID)]
In [81]:
cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method =  'spearman')
cor.test(drug_res$AUC,drug_res$score,alternative = 'less',method =  'spearman')
	Spearman's rank correlation rho

data:  drug_res$LN_IC50 and drug_res$score
S = 50644, p-value = 0.0006783
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.4071687 
	Spearman's rank correlation rho

data:  drug_res$AUC and drug_res$score
S = 49744, p-value = 0.001378
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.3821617 
In [82]:
score_df <- merge(luad_Osimertinib_exp_m,drivers_neg,by = 'V1',all = F)
score <- apply(score_df[,2:61],MARGIN  = 2,FUN = FUN1)

drug_res <- luad_Osimertinib %>% 
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% filter(COSMIC_ID %in% names(score))
drug_res$score = score[as.character(drug_res$COSMIC_ID)]
In [87]:
cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method =  'spearman')
cor.test(drug_res$AUC,drug_res$score,alternative = 'less',method =  'spearman')
	Spearman's rank correlation rho

data:  drug_res$LN_IC50 and drug_res$score
S = 37518, p-value = 0.3734
alternative hypothesis: true rho is less than 0
sample estimates:
        rho 
-0.04245624 
	Spearman's rank correlation rho

data:  drug_res$AUC and drug_res$score
S = 37770, p-value = 0.3534
alternative hypothesis: true rho is less than 0
sample estimates:
        rho 
-0.04945818 
In [89]:
FUN4 <- function(x){
    mean(x*score_df$weight_shap_total_mean)
    #sum(x)
}

score_df <- merge(luad_Osimertinib_exp_m,drivers_pos,by = 'V1',all = F)
score <- apply(score_df[,2:61],MARGIN  = 2,FUN = FUN4)

drug_res <- luad_Osimertinib %>% 
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% filter(COSMIC_ID %in% names(score))
drug_res$score = score[as.character(drug_res$COSMIC_ID)]
In [90]:
cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method =  'spearman')
cor.test(drug_res$AUC,drug_res$score,alternative = 'less',method =  'spearman')
	Spearman's rank correlation rho

data:  drug_res$LN_IC50 and drug_res$score
S = 50644, p-value = 0.0006783
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.4071687 
	Spearman's rank correlation rho

data:  drug_res$AUC and drug_res$score
S = 49744, p-value = 0.001378
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.3821617 
In [93]:
drivers$directions_cal = as.numeric(drivers$directions)
drivers[!drivers$directions,]$directions_cal = -1
In [94]:
drivers
A data.table: 26 × 11
V1weight_shap_total_meanweight_shap_total_stdweight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwaydirectionsdirections_cal
<chr><dbl><dbl><dbl><int><lgl><lgl><lgl><lgl><lgl><dbl>
CD24 0.00083927353.266406e-04-5.656427e-0510FALSEFALSEFALSEFALSEFALSE-1
GTF2B 0.00103246973.080479e-04-1.316672e-0410 TRUEFALSEFALSEFALSEFALSE-1
MSH6 0.00166129934.270973e-04 7.425508e-0510FALSEFALSEFALSEFALSE TRUE 1
TFDP1 0.00137155785.694479e-04-5.785423e-0510 TRUEFALSEFALSEFALSEFALSE-1
MYO1B 0.00101956442.886838e-04 1.748662e-0410FALSEFALSEFALSEFALSE TRUE 1
TUBA1B 0.00183920793.183509e-04-1.412048e-0410FALSEFALSEFALSEFALSEFALSE-1
SLC1A5 0.00219760486.430204e-04 1.082030e-0410FALSEFALSEFALSEFALSE TRUE 1
TGM2 0.00074156612.308803e-04 5.989809e-0610FALSEFALSEFALSEFALSE TRUE 1
BARD1 0.00216733606.107930e-04 1.084314e-0410FALSEFALSEFALSEFALSE TRUE 1
TUBB4B 0.00063758482.468592e-04 1.148352e-0510FALSEFALSEFALSEFALSE TRUE 1
RRBP1 0.00068093032.972012e-04-1.221697e-05 9FALSEFALSEFALSEFALSEFALSE-1
ANXA2 0.00134275096.148865e-04-7.150776e-05 9FALSEFALSEFALSEFALSEFALSE-1
PLK2 0.00113224025.536364e-04 6.720297e-05 9FALSEFALSEFALSEFALSE TRUE 1
PSMC4 0.00092144433.828258e-04-4.451585e-05 9FALSEFALSEFALSEFALSEFALSE-1
DGKE 0.00097306222.876285e-04-5.831443e-05 9FALSEFALSEFALSEFALSEFALSE-1
CEBPD 0.00061215972.758779e-04 7.729752e-05 9 TRUEFALSEFALSEFALSE TRUE 1
HSPA2 0.00069958642.921626e-04-2.506919e-04 8FALSEFALSEFALSEFALSEFALSE-1
RAB31 0.00114500013.613637e-04-5.959134e-05 8FALSEFALSEFALSEFALSEFALSE-1
TFAP2C 0.00024000258.345076e-05 1.130995e-05 7 TRUEFALSEFALSEFALSE TRUE 1
CD9 0.00208977184.177533e-04-1.463574e-04 6FALSEFALSEFALSEFALSEFALSE-1
TPM1 0.00033375731.718587e-04 2.991494e-05 6FALSEFALSEFALSEFALSE TRUE 1
LMO7 0.00060439602.033280e-04 2.352714e-04 6FALSEFALSEFALSEFALSE TRUE 1
KLF5 0.00036248022.617805e-04 1.071805e-04 6 TRUEFALSEFALSEFALSE TRUE 1
TRIB1 0.00047530062.120645e-04 2.118026e-05 6FALSEFALSEFALSEFALSE TRUE 1
HIST1H1C0.00038036971.031536e-04-7.409843e-05 4FALSEFALSEFALSEFALSEFALSE-1
DAAM1 0.00207008054.885557e-04 1.041791e-04 4FALSEFALSEFALSEFALSE TRUE 1
In [96]:
FUN5 <- function(x){
    sum(x*score_df$weight_shap_total_mean*score_df$directions_cal)
    #sum(x)
}
score_df <- merge(luad_Osimertinib_exp_m,drivers,by = 'V1',all = F)
score <- apply(score_df[,2:61],MARGIN  = 2,FUN = FUN5)

drug_res <- luad_Osimertinib %>% 
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% filter(COSMIC_ID %in% names(score))
drug_res$score = score[as.character(drug_res$COSMIC_ID)]
In [97]:
cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method =  'spearman')
cor.test(drug_res$AUC,drug_res$score,alternative = 'less',method =  'spearman')
	Spearman's rank correlation rho

data:  drug_res$LN_IC50 and drug_res$score
S = 47080, p-value = 0.008474
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.3081412 
	Spearman's rank correlation rho

data:  drug_res$AUC and drug_res$score
S = 46064, p-value = 0.01532
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.2799111 

load CCLE¶

In [246]:
CCLE_exp <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/CCLE_DepMap_18Q1_RNAseq_RPKM_20180214.gct')
In [247]:
CCLE_exp
A data.table: 56318 × 1050
NameDescription22RV1_PROSTATE2313287_STOMACH253JBV_URINARY_TRACT253J_URINARY_TRACT42MGBA_CENTRAL_NERVOUS_SYSTEM5637_URINARY_TRACT59M_OVARY639V_URINARY_TRACT⋯PEDS015T_SOFT_TISSUERT11284_URINARY_TRACTSMSCTR_SOFT_TISSUESMZ1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUESW982_SOFT_TISSUESYO1_SOFT_TISSUETC138_BONETC205_BONEUPCISCC152_UPPER_AERODIGESTIVE_TRACTUW228_CENTRAL_NERVOUS_SYSTEM
<chr><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
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ENSG00000237683.5AL627309.1 6.17780 6.566481.034050.768881.012314.030053.292195.23439⋯0.628405.91599 0.905051.956120.472541.12027 0.292440.25216 1.914720.21741
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ENSG00000269981.1RP11-34P13.16 4.10155 5.145820.288740.290750.283612.955162.265594.87078⋯0.206596.24651 1.245892.101970.946771.50955 0.480710.13373 2.180380.49040
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ENSG00000228463.4AP006222.2 1.39546 0.814201.490901.675321.124890.784170.315030.70630⋯0.328920.64095 0.127011.671120.234010.28241 1.201561.14947 0.186830.66111
ENSG00000241670.2AP006222.1 3.42004 1.269044.066194.549503.180341.541351.295312.44463⋯1.010201.04723 0.306574.333170.784300.96816 1.833451.51713 0.179562.14708
ENSG00000237094.7RP4-669L17.10 5.40033 5.152211.806991.600182.118390.974182.479121.61543⋯0.774550.81067 1.592560.554720.738430.42826 0.401350.45318 0.688390.17951
ENSG00000250575.1RP4-669L17.8 5.38639 5.683871.147180.947841.242361.292032.756351.35521⋯1.112831.45587 3.638820.887141.544640.66789 0.561950.72548 1.850070.46692
ENSG00000233653.3CICP7 0.55159 0.209490.158170.107420.112000.062750.093250.27045⋯0.063160.03611 0.050690.019130.003190.01208 0.055120.01533 0.008770.00000
ENSG00000224813.2RP4-669L17.4 2.57880 0.117670.253090.152910.198870.431720.320800.29000⋯0.054330.08282 0.000000.035090.043940.00000 0.075850.14067 0.000000.00000
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ENSG00000236601.1RP4-669L17.2 0.00000 0.000000.000000.000000.000000.000000.000000.00000⋯0.000000.00000 0.000000.000000.000000.00000 0.000000.00000 0.000000.00000
ENSG00000236679.2RP4-669L17.1 0.88719 0.247560.354990.476610.325430.181660.299970.22597⋯0.228590.11616 0.029650.041020.061620.05179 0.070920.29595 0.056430.05565
ENSG00000231709.1RP5-857K21.1 0.52008 0.075460.064930.032690.127540.479920.039190.11366⋯0.011610.03541 0.040670.210070.009390.05920 0.453990.04511 0.025800.00000
ENSG00000235146.2RP5-857K21.2 0.00000 0.000000.000000.000000.000000.000000.000000.00000⋯0.000000.00000 0.113010.031270.000000.00000 0.000000.00000 0.000000.00000
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋱⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
ENSGR0000270726.1AJ271736.10 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000⋯ 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
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ENSG00000211459.2MT-RNR1 910.27307 469.22092 1786.56653 2176.14771 884.79218 301.08878 2614.87988 675.11743⋯1555.49182 1269.64868 377.95169 395.04962 452.11700 973.65546 981.28265 1460.00427 555.72089 291.52655
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ENSG00000210082.2MT-RNR2 5638.01953 1781.06616 7849.1123016147.125982471.765381613.67700 8680.268551838.13623⋯5183.72119 3302.512941707.747311253.85999 855.8330710336.676763690.90479 7264.391112183.910161826.41577
ENSG00000209082.1MT-TL1 1.98519 0.14120 0.24297 0.00000 0.11932 0.82890 0.65993 0.23200⋯ 0.78230 0.19877 0.60874 0.29479 0.10544 0.26587 0.54609 1.85682 0.00000 0.42853
ENSG00000198888.2MT-ND1 2925.43628 1367.65308 1888.47595 2255.65625 984.257631918.51343 2427.981452112.54736⋯2769.03271 2440.453861518.37463 901.30054 381.61768 1091.196291445.38892 3173.048831121.96899 618.01868
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In [248]:
CCLE_info <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/sample_info.csv')
In [249]:
CCLE_info
A data.table: 394 × 15
cell_linen_replicatesclean_cell_line_namecell_line_SSMDSSMD_failureculture_typeculture_mediumculture_codealiasesprimary_tissuehistologyhistology_subtypetypetumor_typeCas9_activity
<chr><int><chr><dbl><lgl><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
143B_BONE 1143B -2.0856446FALSEAdherent EMEM; 10% FBS; 0.015 mg/ml 5-bromo-2'-seoxyuridine 143B bone bone NS osteosarcoma Osteosarcoma 11
253J_URINARY_TRACT 1253J -1.8428910FALSE DMEM: 90.0% 253J urinary_tract urinary_tract carcinoma urinary_tract Bladder 11
42MGBA_CENTRAL_NERVOUS_SYSTEM 242MGBA -1.3369782FALSE RPMI 1640 + EMEM (1:1): 80.0% 42MGBA central_nervous_systemglioma glioma astrocytoma Glioma 27.4
5637_URINARY_TRACT 25637 -1.9308829FALSElight Adherent or suspansionRPMI-1640: 90.0% 5637 4.9
59M_OVARY 259M -1.2884236FALSEAdherent DMEM; 10% FBS + 2 mM Glutamine, sodium pyruvate, ITS 59M ovary ovary carcinoma ovary Ovarian 5.7
639V_URINARY_TRACT 1639V -1.6057682FALSEAdherent DMEM; 10% FBS 639V urinary_tract urinary_tract carcinoma urinary_tract Bladder 20.3
647V_URINARY_TRACT 2647V -1.5523345FALSE DMEM; 15% FBS, 2mMGlutamax-1 647V urinary_tract urinary_tract carcinoma urinary_tract Bladder 14.2
769P_KIDNEY 2769P -1.6189531FALSE RPMI;10% FBS; 769P kidney kidney carcinoma renal_clear_cell Kidney 16.7
786O_KIDNEY 3786O -1.3468784FALSE RPMI; 10% FBS 786-O kidney kidney carcinoma renal_clear_cell Kidney 36.5
8305C_THYROID 28305C -0.9969040FALSE RPMI-1640: 85.0% 8305C thyroid thyroid carcinoma thyroid Thyroid 8.6
8MGBA_CENTRAL_NERVOUS_SYSTEM 28MGBA -0.8486761FALSE EMEM: 80.0% 8MGBA central_nervous_systemglioma glioma astrocytoma Glioma 45.7
A2058_SKIN 2A2058 -1.3985050FALSEAdherent DMEM; 10% FBS A2058 melanoma skin malignant_melanoma melanoma Skin 0
A2780_OVARY 2A2780 -2.4762733FALSE RPMI; 10% FBS A2780 ovary ovary carcinoma ovarian_adenocarcinoma;unlikelyOvarian 16.7
A549_LUNG 4A549 -1.9398550FALSEAdherent DMEM; 10% FBS DMEM001A549 lung_NSC lung non_small_cell_carcinoma lung_non_small Lung 17.2
ABC1_LUNG 3ABC1 -1.9669010FALSE EMEM; 10% FBS ABC-1 lung_NSC lung carcinoma lung_non_small Lung 22.5
AGS_STOMACH 2AGS -1.6651098FALSE F12K; 10% FBS AGS stomach stomach adenocarcinoma gastric_adenocarcinoma Gastric 16.3
ASPC1_PANCREAS 4ASPC1 -0.8058231FALSE RPMI; 10% FBS RPMI001AsPC-1 pancreas pancreatic_exocrine pancreatic_ductal_adenocarcinomapancreas_carcinoma Pancreas 57.2
AU565_BREAST 2AU565 -1.4317646FALSEadherent DMEM; 10% FBS AU565 breast breast Luminal breast_carcinoma Breast 36.2
BC3C_URINARY_TRACT 2BC3C -1.3567518FALSEAdherent M10 BC3C urinary_tract urinary_tract carcinoma urinary_tract Bladder 4.9
BFTC905_URINARY_TRACT 2BFTC905-1.3863104FALSE DMEM: 90.0% BFTC905urinary_tract urinary_tract carcinoma urinary_tract Bladder 38.8
BFTC909_KIDNEY 2BFTC909-1.0006993FALSE DMEM;10 % FBS; BFTC909kidney kidney carcinoma renal_carcinoma Kidney 38.8
BHY_UPPER_AERODIGESTIVE_TRACT 2BHY -1.2361349FALSEAdherent DMEM;10% FBS; BHY upper_aerodigestive upper_aerodigestive_tractcarcinoma head_and_neck_squamous Head and Neck8.8
BICR22_UPPER_AERODIGESTIVE_TRACT2BICR22 -1.5166298FALSEAdherent DMEM; 10% FBS; 2mM Glutamine; 0.4ug/ml hydrocortisone BICR 22upper_aerodigestive upper_aerodigestive_tractcarcinoma head_and_neck_squamous Head and Neck5.6
BICR56_UPPER_AERODIGESTIVE_TRACT2BICR56 -1.9335539FALSE DMEM; 10% FBS; 2mM Glutamine; 0.4ug/ml hydrocortisone BICR 56upper_aerodigestive upper_aerodigestive_tractcarcinoma head_and_neck_squamous Head and Neck19.3
BICR6_UPPER_AERODIGESTIVE_TRACT 2BICR6 -1.1369064FALSEAdherent DMEM;10% FBS; BICR6 upper_aerodigestive upper_aerodigestive_tractcarcinoma head_and_neck_squamous Head and Neck10.6
BIN67_OVARY 2BIN67 -1.4820323FALSE 20% FBS, 40% DMEM, 40% DMEM/F12 BIN-67 ovary ovarian_rhabdoid SCCOHT rhabdoid_tumor Rhabdoid 57
BT549_BREAST 3BT549 -1.3533033FALSEAdherent RPMI-1640: 10% heat inactivated FBS BT-549 breast breast Basal breast_carcinoma Breast 22.6
C2BBE1_LARGE_INTESTINE 3C2BBE1 -1.1272783FALSE D10+ITS+Glu C2BBe1 large_intestine colorectal adenocarcinoma colon_carcinoma Colon 5.8
C32_SKIN 2C32 -1.0697073FALSE EMEM; 10% FBS; 0.1mM NEAA C32 melanoma skin malignant_melanoma melanoma Skin 33.9
CAKI1_KIDNEY 2CAKI1 -1.3984047FALSE McCoy's 5A; 10% FBS Caki-1 kidney kidney carcinoma renal_clear_cell Kidney 49.5
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
TF1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 3TF1 -1.3036890FALSEsuspensionRPMI-1640: 10%FBS; 2ng/ml GM-CSF TF1 AML haematopoietic_and_lymphoid_tissuehaematopoietic_neoplasmAML AML good
THP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE3THP1 -0.9557363FALSEsuspensionRPMI-1640: 10%FBS; BME (0.05mM) THP-1 AML haematopoietic_and_lymphoid_tissuehaematopoietic_neoplasmAML AML 34.4
TOV21G_OVARY 3TOV21G -2.1112380FALSE MCDB 105:Medium 199 (1:1); 15% FBS TOV-21G ovary ovary carcinoma ovarian_clear_cell; hypermutatedOvarian 37.7
TUHR10TKB_KIDNEY 2TUHR10TKB -1.1968951FALSEAdherent RPMI-1640: 90.0% TUHR10TKB kidney kidney carcinoma renal_carcinoma Kidney 54.8
TUHR4TKB_KIDNEY 1TUHR4TKB -1.0834388FALSEAdherent DMEM;10% FBS; TUHR4TKB kidney kidney carcinoma renal_carcinoma Kidney 22.8
U118MG_CENTRAL_NERVOUS_SYSTEM 2U118MG -1.0066358FALSE DMEM; 10% FBS U-118 MG central_nervous_systemglioma glioma astrocytoma Glioma 30.9
U178_CENTRAL_NERVOUS_SYSTEM 2U178 -0.7408038FALSE DMEM; 10% FBS U178 glioma central_nervous_system glioma glioblastoma Glioblastoma 51.7
U251MG_CENTRAL_NERVOUS_SYSTEM 2U251MG -2.2653716FALSE DMEM; 10% FBS U251-MG central_nervous_systemglioma glioma astrocytoma Glioma 11.3
U2OS_BONE 2U2OS -1.5726655FALSEAdherent McCoy's 5A; 10% FBS U2-OS bone bone osteosarcoma osteosarcoma Osteosarcoma 52.6
U343_CENTRAL_NERVOUS_SYSTEM 2U343 -1.5622078FALSE DMEM; 10% FBS U343 glioma central_nervous_system glioma glioblastoma Glioblastoma 17.8
U87MG_CENTRAL_NERVOUS_SYSTEM 2U87MG -0.7999304FALSE EMEM; 10% FBS U-87 MG central_nervous_systemglioma glioma astrocytoma Glioma 37.9
U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE3U937 -1.2477302FALSEsuspensionRPMI;10% FBS; U-937 lymphoma_DLBCL haematopoietic_and_lymphoid_tissuelymphoid_neoplasm AML AML 23.9
UACC257_SKIN 1UACC257 -1.2689274FALSEAdherent RPMI-1640; 10%FBS RPMI001UACC257 melanoma skin malignant_melanoma melanoma Skin 18.7
UACC62_SKIN 2UACC62 -1.1422030FALSE RPMI-1640: 10%FBS UACC-62 melanoma skin malignant_melanoma melanoma Skin 26.9
UMCHOR1_BONE 3UM-CHOR1 -1.2465131FALSE Um-Chor1 bone chordoma sarcoma sarcoma Bone 10.3
UMUC1_URINARY_TRACT 2UMUC1 -1.4589930FALSE EMEM; 10% FBS, 2mMGlutamax-1, 1xNEAA UMUC1 urinary_tract urinary_tract carcinoma urinary_tract Bladder 33.7
UMUC3_URINARY_TRACT 2UMUC3 -1.4348565FALSE EMEM; 10% FBS UMUC3 urinary_tract urinary_tract carcinoma urinary_tract Bladder 11.7
UOK101_KIDNEY 2UOK101 -1.3615701FALSEAdherent DMEM; 10% FBS UOK101 kidney kidney carcinoma renal_clear_cell Kidney 38.7
UPCISCC152_UPPER_AERODIGESTIVE_TRACT 2UPCISCC152-1.0926038FALSE EMEM; 10% FBS, 2mMGlutamax-1 UPCI:SCC152upper_aerodigestive upper_aerodigestive_tract carcinoma head_and_neck_squamous Head and Neck NO VALUES 8.16.16
UW228_CENTRAL_NERVOUS_SYSTEM 1UW228 -1.5177330FALSE D:F10+Glu UW228 central_nervous_systemcentral_nervous_system medulloblastoma medulloblastoma Medulloblastoma12.3
VMCUB1_URINARY_TRACT 2VMCUB1 -1.5920114FALSE DMEM: 90.0%; 10%FBS VMCUB1 urinary_tract urinary_tract carcinoma urinary_tract Bladder 32.8
WM115_SKIN 2WM115 -0.9006826FALSEAdherent EMEM: 10% FBS WM-115 melanoma skin malignant_melanoma melanoma Skin 51.7
WM1799_SKIN 2WM1799 -0.9749160FALSE0 RPMI-1640: 10%FBS WM1799 melanoma skin malignant_melanoma melanoma Skin 30.8
WM2664_SKIN 2WM2664 -1.5681949FALSE DMEM; 10% FBS WM-266-4 melanoma skin malignant_melanoma melanoma Skin 28.5
WM793_SKIN 2WM793 -1.5361338FALSE RPMI-1640: 10%FBS WM-793 melanoma skin malignant_melanoma melanoma Skin 45
WM983B_SKIN 2WM983B -1.2323335FALSE RPMI-1640: 10%FBS WM-983B melanoma skin malignant_melanoma melanoma Skin 13.1
YAPC_PANCREAS 4YAPC -1.4144762FALSE RPMI-1640: 10%FBS pancreas pancreatic_exocrine adenocarcinoma pancreas_carcinoma Pancreas 7
YD38_UPPER_AERODIGESTIVE_TRACT 2YD38 -1.3743563FALSEAdherent RPMI-1640; 10%FBS YD-38 upper_aerodigestive upper_aerodigestive_tract carcinoma head_and_neck_squamous Head and Neck 16.1
YKG1_CENTRAL_NERVOUS_SYSTEM 2YKG1 -1.3185945FALSEAdherent DMEM; 10% FBS YKG1 central_nervous_system glioblastoma Glioblastoma 21.1
ZR751_BREAST 2ZR751 -1.5258912FALSEAdherent RPMI; 10% FBS ZR-75-1 breast breast Luminal breast_carcinoma Breast 20.9
In [250]:
table(CCLE_info$tumor_type)
                              AML          Bladder             Bone 
               1               10               20                3 
          Breast         Cervical            Colon      Endometrial 
              26                1               25               14 
      Esophageal          Ewing's          Gastric     Glioblastoma 
              10                4               11               20 
          glioma           Glioma    Head and Neck           Kidney 
               1                9               12               17 
           Liver             Lung         Lymphoma  Medulloblastoma 
              13               48                8                7 
    Mesothelioma          Myeloma    Neuroblastoma     Osteosarcoma 
               1                7               15                5 
           Other          Ovarian         Pancreas         Rhabdoid 
               9               31               22                3 
             RMS          Sarcoma             Skin Synovial Sarcoma 
               6                1               29                2 
           T-ALL          Thyroid 
               2                1 
In [251]:
CCLE_drug <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/CCLE_NP24.2009_Drug_data_2015.02.24.csv')
In [252]:
CCLE_drug
A data.table: 11670 × 13
CCLE Cell Line NamePrimary Cell Line NameCompoundTargetDoses (uM)Activity Data (median)Activity SDNum DataFitTypeEC50 (uM)IC50 (uM)AmaxActArea
<chr><chr><chr><chr><chr><chr><chr><int><chr><dbl><dbl><dbl><dbl>
1321N1_CENTRAL_NERVOUS_SYSTEM 1321N1 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,88.67,11.0,2.16,.27,-10,-13,-26,-43 3.31,3.72,5.36,4.67,13.1,.18,2.42,7.51 8Sigmoid 8.71777368.0000000 -42.5580140.7124
22RV1_PROSTATE 22Rv1 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,8.94,12.5,-14,4.16,-25,-32,-52,-71 1.95,13.3,6.98,21.8,16.0,18.8,4.84,7.938Sigmoid 8.16516362.3299241 -71.5893401.6723
42MGBA_CENTRAL_NERVOUS_SYSTEM 42-MG-BAAEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,88.91,8.39,-3.5,12.4,-.55,-6.2,-48,-6313.7,7.70,11.1,6.43,24.0,9.57,9.57,10.48Sigmoid 1.51450852.6821299 -63.4913711.1852
5637_URINARY_TRACT 5637 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,82.15,9.91,-3.5,.056,-2.1,-14,-30,-62 4.05,9.75,12.7,4.36,11.0,10.0,24.6,.14 8Sigmoid 8.00659525.0023141 -62.3527760.9948
639V_URINARY_TRACT 639-V AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,811.8,-7.3,-9.4,-15,-11,-21,-53,-50 .95,5.67,11.1,.68,31.6,22.3,1.45,3.08 8Sigmoid 0.93119571.7361814 -51.9598081.5436
697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 697 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,818.4,4.65,8.64,19.3,-19,-20,-21,-78 25.4,1.92,15.0,23.1,24.7,13.5,1.26,2.518Sigmoid 8.70065454.2608218 -73.3337861.7665
769P_KIDNEY 769-P AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,82.58,-4.1,6.18,-7.0,-13,-40,-28,-47 18.1,12.4,12.4,5.80,5.24,10.3,6.56,6.848Sigmoid 0.30624288.0000000 -39.6880951.4376
786O_KIDNEY 786-O AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,8-1.2,-.53,.55,-4.1,2.41,-11,-8.8,-52 14.5,15.3,7.11,9.90,4.66,3.60,2.58,11.58Sigmoid 5.26766737.6131477 -51.6863170.5954
8305C_THYROID 8305C AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,81.00,-1.9,8.83,9.38,-18,-17,-28,-68 6.49,3.67,4.67,6.24,6.70,3.11,9.45,7.528Sigmoid 8.52077154.9506359 -66.2288361.2929
8505C_THYROID 8505C AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,8-6.0,-.21,-2.4,6.80,-6.3,1.35,-19,-22.63,13.0,4.25,6.36,3.61,7.19,6.61,2.70 8Sigmoid 1.84668998.0000000 -22.2688260.3189
8MGBA_CENTRAL_NERVOUS_SYSTEM 8-MG-BA AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,8-6.0,-23,-17,9.18,-2.6,-20,-34,-91 7.42,9.33,2.72,5.46,28.5,1.39,3.31,1.078Sigmoid 8.21299653.8883409 -91.3399811.5025
A172_CENTRAL_NERVOUS_SYSTEM A172 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,8-.27,.084,-2.7,-6.1,-6.3,-6.1,-15,-225.84,1.59,2.77,5.89,2.43,3.25,4.54,7.738Sigmoid 8.41962158.0000000 -21.3667830.3615
A204_SOFT_TISSUE A-204 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,83.12,2.35,2.69,-5.1,6.80,-24,-6.7,-685.56,3.35,2.22,2.93,2.93,2.31,1.72,12.58Sigmoid 4.16371205.1503119 -67.1932530.5525
A2058_SKIN A2058 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,84.97,-2.9,1.30,1.23,-1.3,-9.4,-12,-933.91,.85,4.79,1.27,4.97,7.31,6.77,2.91 8Sigmoid 8.73129705.5130911 -92.3689501.0006
A253_SALIVARY_GLAND A-253 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,82.93,3.40,.080,-4.7,-1.6,-24,-12,-22 4.57,16.8,1.79,.29,1.18,1.76,4.17,7.84 8Constant NA8.0000000 -4.6887730.6375
A2780_OVARY A2780 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,86.95,1.20,-.26,1.52,-3.7,-14,-19,-47 10.9,9.43,4.29,8.48,2.84,9.36,3.68,17.78Sigmoid 8.73567076.5089040 -57.1805730.6674
A375_SKIN A-375 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,81.92,4.74,4.50,-6.6,-7.7,-27,-47,-80 2.97,1.72,.44,4.55,2.36,2.60,1.62,1.18 8Sigmoid 8.04399442.7554235 -79.6080701.4484
A549_LUNG A549 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,85.54,5.62,-.69,-7.7,-.50,-24,-60,-1022.46,.16,6.07,.64,3.96,5.91,2.04,.94 8Sigmoid 3.29913121.9547869-101.6332171.6406
A673_BONE A-673 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,8-3.6,5.42,-4.6,19.0,-6.1,-42,-55,-70 9.41,4.84,6.69,13.4,5.04,5.36,5.28,4.398Sigmoid 0.62764611.1276772 -65.0293431.5990
ACHN_KIDNEY ACHN AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,83.24,9.14,-4.2,4.02,-1.5,-10,-11,-53 1.52,5.92,2.67,.47,2.59,3.12,1.67,5.78 8Sigmoid 8.70985227.6729093 -52.1425480.6207
ALLSIL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUEALL-SIL AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,8-1.8,2.14,1.10,12.0,-17,-10,-50,-78 3.85,5.52,5.32,15.4,6.82,1.01,7.05,3.448Sigmoid 2.11701612.5584965 -78.5075071.8822
AMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE AMO-1 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,85.90,13.2,-14,-7.1,-13,-45,-80,-99 21.3,9.53,3.20,13.0,34.1,23.9,6.67,.26 8Linear NA0.1945099 -98.8428962.8999
AN3CA_ENDOMETRIUM AN3 CA AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,819.0,15.0,4.05,-1.4,1.59,-8.2,-12,-143.66,6.75,13.3,26.7,14.1,9.39,6.78,7.748Sigmoid 0.27500518.0000000 -15.0364250.3505
ASPC1_PANCREAS AsPC-1 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,84.82,2.54,-4.9,-.16,-13,-30,-67,-80 12.1,16.4,10.8,17.2,.96,17.0,3.41,5.90 8Sigmoid 1.18146551.4277748 -77.2125851.3966
AU565_BREAST AU565 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,815.3,5.67,7.83,3.45,-12,-5.9,-20,-43 20.4,4.88,21.4,12.2,16.7,3.44,9.70,1.598Sigmoid 9.06517528.0000000 -40.8011860.7651
AZ521_STOMACH AZ-521 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,89.23,-2.4,8.19,-30,-18,-52,-58,-89 3.53,4.18,4.04,3.50,26.5,12.6,7.65,7.728Sigmoid 8.54288881.1525109 -87.2100602.3462
BCPAP_THYROID B-CPAP AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,8-1.2,1.18,.057,-2.1,-18,-18,-24,-70 4.67,5.92,2.38,7.66,.58,1.62,.76,41.8 8Sigmoid 8.88907635.1052547 -67.5041581.3166
BDCM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE BDCM AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,8-16,4.32,-20,-3.4,-5.7,21.7,-13,-51 24.2,18.6,2.31,11.1,.48,4.36,27.4,9.40 8Linear NA4.9726346 -52.2460940.3316
BFTC909_KIDNEY BFTC-909AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,82.87,-3.2,-1.9,-11,-12,-17,-18,-47 2.83,6.38,4.83,3.66,10.0,6.03,6.80,1.608Sigmoid 8.83999918.0000000 -43.8878100.8980
BGC823_STOMACH BGC-823 AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,8.027,1.62,8.48,-2.4,-10,-35,-48,-81 9.29,1.79,6.72,9.61,8.35,4.59,1.26,1.618Sigmoid 6.21321062.3974571 -79.7396551.5611
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
TE15_OESOPHAGUS TE-15 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,827.5,21.7,16.2,11.6,-8.1,-47,-72,-79 8.28,29.9,34.4,16.6,17.1,5.79,2.43,1.538Sigmoid 0.41233550.8777253-79.58098601.67700
TE5_OESOPHAGUS TE-5 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,84.60,8.55,8.76,9.92,1.44,5.70,-12,-15 5.08,3.61,1.28,3.55,7.63,6.59,4.44,8.508Sigmoid 1.53448678.0000000-14.77249430.23000
TE617T_SOFT_TISSUE TE 617.T ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,85.00,21.3,-1.5,11.9,.86,-23,-70,-88 7.93,21.7,3.87,22.6,26.0,4.65,2.36,1.158Sigmoid 1.35957771.5173497-88.69744111.72010
TE9_OESOPHAGUS TE-9 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,8.18,13.1,-6.1,6.61,-7.4,2.33,-35,-45 1.99,16.8,8.36,12.4,2.42,20.8,.31,12.8 8Sigmoid 1.98075388.0000000-45.52315140.92590
TEN_ENDOMETRIUM TEN ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,8-.25,14.3,4.08,9.07,-4.0,4.08,-13,14.2 13.4,15.7,15.5,29.8,8.45,.32,15.1,4.28 8Constant NA8.0000000 3.83914880.08303
TOLEDO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUEToledo ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,8-2.4,14.1,-5.6,.54,3.10,-11,10.0,-3.8 2.19,24.3,15.3,4.00,44.3,20.2,10.1,32.48Constant NA8.0000000 0.67503070.01286
TOV112D_OVARY TOV-112D ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,8-1.5,1.69,-12,-17,-2.1,-13,-13,-47 9.04,7.23,15.7,7.13,3.82,9.99,16.5,3.528Sigmoid 8.39237478.0000000-46.47077940.87770
TOV21G_OVARY TOV-21G ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,811.0,1.04,16.7,2.26,-4.4,-1.4,-19,-57 2.41,2.46,6.73,2.72,2.55,3.55,2.99,10.28Sigmoid 8.53950646.9190626-56.10400010.88570
TT2609C02_THYROID TT2609-C02ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,830.1,18.2,1.56,28.8,-5.0,5.53,-31,-47 36.4,1.63,14.5,16.0,11.2,21.1,21.8,13.88Sigmoid 2.47895158.0000000-48.07135010.82220
TYKNU_OVARY TYK-nu ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,820.3,-1.9,15.1,-6.4,13.9,-.18,28.6,-14 12.9,1.56,12.7,11.0,7.84,12.3,17.4,5.138Constant NA8.0000000 7.28427700.20760
U118MG_CENTRAL_NERVOUS_SYSTEM U-118 MG ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,8-3.4,-7.6,-.63,-.024,10.3,-8.3,.32,-6.511.3,15.4,1.85,1.75,.18,10.7,11.2,12.5 8Constant NA8.0000000 -0.29801790.05681
U2OS_BONE U-2 OS ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,82.79,5.16,2.32,2.76,-8.9,-10,-5.4,-28 1.18,15.3,5.45,6.35,5.14,.31,6.00,17.9 8Sigmoid 8.83289058.0000000-25.13688090.48880
U87MG_CENTRAL_NERVOUS_SYSTEM U-87 MG ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,87.54,4.25,16.3,-2.3,10.2,16.7,8.94,15.412.2,5.84,7.91,21.8,6.70,2.95,16.1,9.948Constant NA8.0000000 11.64905550.07387
U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE U-937 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,810.6,3.38,16.2,18.5,5.05,-9.5,-15,-21 4.88,6.03,4.61,12.1,10.7,9.62,1.64,16.68Sigmoid 2.03052768.0000000-31.24400900.54950
UACC257_SKIN UACC-257 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,85.15,5.38,5.97,8.88,4.36,12.4,-3.6,-2.12.34,7.66,.52,6.22,.72,4.02,1.72,2.50 8Constant NA8.0000000 4.78883080.00000
UACC62_SKIN UACC-62 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,810.8,1.57,-.13,-3.2,-7.8,-16,-24,-51 10.3,6.66,7.20,3.85,9.92,12.1,4.80,6.578Sigmoid 8.61422058.0000000-48.51916500.68730
UACC812_BREAST UACC-812 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,810.7,14.4,-15,12.7,9.02,14.9,-13,-43 17.1,35.3,24.8,26.3,30.8,25.5,51.0,42.18Linear NA0.9121245-71.85778050.44330
UMUC3_URINARY_TRACT UM-UC-3 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,8-.80,7.97,5.99,4.26,8.03,3.29,4.63,2.2214.3,1.50,3.14,.31,3.59,2.20,2.17,.72 8Constant NA8.0000000 4.61356640.00000
VMRCLCD_LUNG VMRC-LCD ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,833.9,21.8,12.5,23.1,51.2,6.46,19.9,-12 8.91,4.00,9.78,9.23,28.5,8.53,27.8,.88 8Constant NA8.0000000 19.12427330.25470
VMRCRCW_KIDNEY VMRC-RCW ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,8-8.3,-6.0,11.8,-.80,21.7,2.57,.021,-26 15.8,1.05,1.64,4.76,6.68,11.6,17.1,16.88Sigmoid 4.22767408.0000000-26.24533270.19400
VMRCRCZ_KIDNEY VMRC-RCZ ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,81.09,8.22,11.8,-5.1,11.3,1.30,-15,-24 3.08,15.8,25.2,8.47,11.0,9.54,.40,8.20 8Sigmoid 2.17851338.0000000-23.61635780.90170
WM115_SKIN WM-115 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,813.5,2.34,10.1,5.14,11.9,-2.7,2.68,-26 10.3,20.1,2.86,5.23,3.62,21.2,23.6,6.398Sigmoid 2.25573288.0000000-26.29893880.10350
WM1799_SKIN WM1799 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,86.35,8.58,28.7,11.9,1.05,-8.2,-8.8,-42 6.96,10.3,15.1,5.25,4.21,15.5,11.9,.88 8Sigmoid 8.49419128.0000000-40.77984240.68190
WM2664_SKIN WM-266-4 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,8-1.6,9.92,-4.9,6.90,-2.8,8.43,-12,-1.3 7.77,3.55,1.71,4.37,10.9,5.51,12.5,13.18Constant NA8.0000000 3.80581260.08816
WM793_SKIN WM-793 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,8-.050,3.17,.15,2.59,-4.0,10.3,-12,-17 14.0,16.4,4.83,11.5,1.51,17.5,6.24,17.98Constant NA8.0000000 -4.04069760.07185
WM88_SKIN WM-88 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,81.99,-1.1,6.73,13.1,-4.1,-4.3,2.53,-14 3.25,3.41,7.44,11.5,.45,6.40,1.28,5.15 8Constant NA8.0000000 0.59260090.24200
WM983B_SKIN WM-983B ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,817.2,15.1,4.57,26.9,-3.3,-2.0,-2.8,-3.124.1,24.5,18.1,21.7,5.15,8.95,9.30,48.28Constant NA8.0000000 2.24138310.98100
YKG1_CENTRAL_NERVOUS_SYSTEM YKG1 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,81.31,-4.4,-2.9,-1.3,-.39,-5.0,-9.3,-16 1.72,3.30,6.16,2.33,4.65,4.73,3.47,2.828Constant NA8.0000000 -3.17190000.07841
ZR751_BREAST ZR-75-1 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,839.3,-25,31.8,25.5,10.6,-37,-22,-37 38.8,7.84,7.22,3.57,.86,23.7,18.6,8.01 8Linear NA8.0000000-34.83546071.17170
ZR7530_BREAST ZR-75-30 ErlotinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,8-23,9.04,8.59,26.0,17.9,12.1,-11,-22 15.7,1.31,1.58,22.9,26.8,23.5,32.8,19.98Linear NA8.0000000-22.05388260.03998
In [253]:
any(CCLE_drug$Compound == 'Osimertinib')
FALSE
In [254]:
table(CCLE_drug$Compound)
      17-AAG       AEW541      AZD0530      AZD6244    Erlotinib   Irinotecan 
         503          503          504          503          503          317 
    L-685458    Lapatinib       LBW242    Nilotinib     Nutlin-3   Paclitaxel 
         491          504          503          420          504          503 
Panobinostat   PD-0325901   PD-0332991    PF2341066   PHA-665752      PLX4720 
         500          504          434          504          503          496 
      RAF265    Sorafenib       TAE684       TKI258    Topotecan      ZD-6474 
         460          503          504          504          504          496 
In [255]:
table(CCLE_drug$Target)
  ABL   ALK c-MET  CDK4  EGFR  FGFR    GS  HDAC HSP90 IGF1R  MDM2   MEK   RAF 
  924   504  1007   434  1503   504   491   500   503   503   504  1007   956 
  RTK  TOP1 TUBB1  XIAP 
  503   821   503   503 
In [256]:
CCLE_drug %>% filter(CCLE_drug$Target == 'EGFR') %>% dplyr::select(Compound) %>% table()
Compound
Erlotinib Lapatinib   ZD-6474 
      503       504       496 
In [257]:
CCLE_drug_info <- readxl::read_excel('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/CCLE_GNF_data_090613.xls')
In [258]:
CCLE_drug_info
A tibble: 26 × 207
GNF_REG_IDcompound nameother nameDESCRIPTION563722Rv1786-OA-204A-253A2780⋯THP-1ToledoTOV-112DU-87MGU-937UACC-257VMRC-RCWWM-266-4YH13ZR-75-1
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>⋯<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
GNF-00-0325-0295-7PHA-665752 NA [Pha-665752,5-((2,6-dichlorobenzyl)sulfonyl)-3-((3,5-dimethyl-4-((2-(pyrrolidin-1-ylmethyl)pyrrolidin-1-yl)carbonyl)-1H-pyrrol-2-yl)methylene)-1,3-dihydro-2H-indol-2-one][CAS:477575-56-7][MOA:Apoptosis-inducer; Cytostatic; Tyrosine-kinase-inhibi ...] >20 >20 9.0899999999999999 >20 >20 >20 ⋯>20 13.24 >20 >20 >20 16.489999999999998 18.809999999999999 18.170000000000002 >20 >20
GNF-00-0519-9793-4AZD6244 Selumetinib [Selumetinib,AZD 6244][MOA:Apoptosis-inducer; Cytostatic; Mek-1-protein-kinase-inhibitor][Target:Mek1][GNF Document:ALK\GNF March 2009 MEKEFF.ppt, B-Raf\2008-10-14-MEKFF-ODB-final.ppt][Patent:N3 alkylated benzimidazole derivatives as mek inhibit ...]>20 >20 >20 >20 0.16059999999999999 >20 ⋯>20 >20 >20 >20 >20 0.01257 >20 0.018239999999999999 >20 >20
GNF-00-0520-3101-3Tozasertib VX680 [Tozasertib,Vx680][CAS:639089-54-6][MOA:Abl-tyrosine-kinase-inhibitor; Apoptosis-inducer; Cytostatic; Synergist; Tyrosine-kinase-inhibitor][Indication:Aurora kinase inhibitor highest phase:Phase 2 - Acute lymphoblastic leukemia, Chronic myelocyt ...] 0.2094 0.051499999999999997 0.64100000000000001 0.025059999999999999 0.065000000000000002 0.067500000000000004⋯0.02307 0.061800000000000001 0.029090000000000001 0.1113 0.024039999999999999 0.71099999999999997 6.1500000000000004 0.048899999999999999 0.0468000000000000010.033500000000000002
GNF-00-0526-5527-3Nutlin-3 NA [Analog][nutlin-3a,nutlin-3][CAS:548472-68-0][MOA:Apoptosis-inducer; Cell-cycle-inhibitor; Cytostatic; Mdm2-p53-binding-protein-inhibitor; Ubiquitin-ligase-inhibitor][Target:P53 kinase, MDM2/p53, Mdm2, MDM2 oncogene, MDM2 Oncogene][GNF Document: ...] >20 9.7300000000000004 >20 3.5600000000000001 >20 14.24 ⋯>20 >20 15.83 >20 >20 1.1060000000000001 >20 1.252 >20 2.2650000000000001
GNF-00-0526-5536-4Sorafenib Nexavar [Sorafenib(in Wikipedia),Nexavar][CAS:284461-73-0,475207-59-1][MOA:Antineoplastic Agents; Protein Kinase Inhibitors][Indication:For the treatment of patients with advanced renal cell carcinoma.][Toxicity:Most of the dose-limiting toxicities incl ...] >20 >20 >20 1.631 >20 >20 ⋯>20 >20 >20 >20 >20 >20 >20 >20 >20 >20
GNF-00-0526-5570-6PF-2341066 Crizotinib [pf-2341066][MOA:Apoptosis-inducer; Cyclic-amp-agonist; Cytostatic][Indication:C-MET tyrosine kinase antagonist highest phase:Phase 1 - Cancer, Stomach tumor][Target:Hgfr, Hepatocyte growth factor receptor, C-MET][GNF Document:ROS\Ros07-02-08MX. ...] 9.1400000000000006 2.915 8.1099999999999994 1.248 4.4299999999999997 4.1100000000000003 ⋯0.97299999999999998 1.464 0.39600000000000002 >20 0.79200000000000004 6.8700000000000001 14.18 3.54 7.6600000000000001 7.0199999999999996
GNF-00-0526-9948-6Staurosporine NA [Staurosporine(in Wikipedia),Sch-47112][CAS:62996-74-1,622996-74-1][MOA:Enzyme Inhibitors][Indication:Protein kinase C inhibitor; CDK1 inhibitor; CDK4 inhibitor highest phase:Research Tool - Cancer, Hypertension; Platelet biosynthesis stimulant; ...] 0.00764000000000000010.00963999999999999930.0045599999999999998<0.001221 0.037199999999999997 0.0935 ⋯0.0064200000000000004<0.001221 0.00702000000000000020.00992999999999999960.00559000000000000040.033300000000000003 0.00606999999999999990.0064799999999999996<0.001221 0.038399999999999997
GNF-00-0527-1619-5Docetaxel Taxotere [Docetaxel(in Wikipedia),Taxotere][CAS:114977-28-5,148408-66-6][MOA:Antineoplastic Agents][Indication:For the treatment of patients with locally advanced or metastatic breast cancer after failure of prior chemotherapy. Also used as a single agen ...] <0.001221 <0.001221 <0.001221 <0.001221 <0.001221 <0.001221 ⋯<0.001221 <0.001221 <0.001221 <0.001221 <0.001221 >20 0.018519999999999998 <0.001221 <0.001221 0.00415
GNF-00-0527-1633-3Paclitaxel Taxol [Paclitaxel(in Wikipedia),Paclitaxel - MGI GP][CAS:33069-62-4,197778-55-5][MOA:Antineoplastic Agents Phytogenic; Tubulin Modulators][Warning:Discontinued-II][Indication:Paclitaxel is a potent inhibitor of eukaryotic cell replication, blocking ce ...] <0.001221 <0.001221 0.01102 <0.001221 <0.001221 <0.001221 ⋯NoFit <0.001221 <0.001221 NoFit <0.001221 NoFit 0.069699999999999998 <0.001221 <0.001221 <0.001221
GNF-00-0527-1635-5L-685458 NA [L-682679,L-685458][CAS:126409-24-3,132565-31-2][MOA:Virucide; Gamma-secretase-inhibitor][Target:HIV Protease, Gamma secretase, Beta-amyloid protein, Secretase, Gamma secretase, Gamma Secretase][Crystal:1IZH|Inhibitor Of Hiv Protease With Unusua ...] >20 >20 >20 >20 >20 >20 ⋯>20 >20 >20 >20 >20 >20 >20 >20 >20 >20
GNF-00-0527-1636-6Irinotecan Camptosar  [Irinotecan(in Wikipedia),CP0][CAS:100286-90-6,111348-33-5][MOA:Antineoplastic Agents Phytogenic; Enzyme Inhibitors][Indication:For the treatment of metastatic colorectal cancer (first-line therapy when administered with 5-fluorouracil and leuco ...] 0.033500000000000002 0.061699999999999998 0.089399999999999993 0.039199999999999999 0.086800000000000002 0.087800000000000003⋯0.1764 0.046399999999999997 0.1191 0.1308 0.1338 0.054100000000000002 0.047500000000000001 0.035099999999999999 0.0850999999999999950.048000000000000001
GNF-00-0527-1637-7Topotecan Hycamtin [Topotecan(in Wikipedia),Sn-38(in Wikipedia)][CAS:119413-54-6,123948-87-8][MOA:Antineoplastic Agents; Enzyme Inhibitors; Antineoplastic Agents Phytogenic][Warning:Discontinued on 1993/09/15!1992/12/15!1992/02/15!1991/11/15!1991/11/15!1990/09/15; ...] 0.023820000000000001 0.010449999999999999 0.00827999999999999920.00489000000000000020.023349999999999999 0.03014 ⋯0.060699999999999997 0.00515000000000000010.038399999999999997 0.044499999999999998 0.059200000000000003 0.024819999999999998 0.019369999999999998 0.02213 0.01821 <0.001221
GNF-00-0527-1831-7Cisplastin NA [Indication:Cisplatin is a cytotoxic whose main mode of action is formation of adducts with both nuclear and mitochondrial DNA. This involves the formation of intrastrand DNA cross-links between guanosine residues. Cisplatin is usually cell cycl ...] >20 >20 >20 >20 >20 >20 ⋯>20 >20 >20 >20 >20 >20 >20 >20 >20 >20
GNF-00-0558-2575-7AEW541 NA [Adw-742][MOA:Apoptosis-inducer; Cytostatic; Insulin-like-growth-factor-1-antagonist][Indication:Insulin like growth factor receptor type I antagonist highest phase:Phase 1 - Cancer; Discovery - Multiple myeloma][Target:Igf1r, Insulin-like growt ...] >20 8.3300000000000001 12.609999999999999 10.02 16.25 >20 ⋯7.4299999999999997 10.460000000000001 8.5099999999999998 >20 12.199999999999999 9.1199999999999992 11.74 13.09 13.69 >20
GNF-00-0558-2578-0TAE684 NA [Nvp-tae-684][Novarits CSP/sPoC:GNF sPOC regarding ALK Inhibitor part 2 (268) approved on 2004-08-11; GNF CSP regarding ALK Inhibitor part 2 (268) approved on 2004-03-11][MOA:Alk-tyrosine-kinase-receptor-inhibitor; Apoptosis-inducer; Cytosta ...] 4.6900000000000004 3.29 6.8899999999999997 1.625 4.0999999999999996 4.1900000000000004 ⋯1.3080000000000001 1.5249999999999999 0.748 11.31 2.4119999999999999 5.6900000000000004 >20 4.6500000000000004 1.4530000000000001 5.1500000000000004
GNF-00-0558-2660-3Panobinostat  Faridak [Lbh-589,Panobinostat][CAS:404950-80-7][MOA:Angiogenesis-inhibitor; Apoptosis-inducer; Cytostatic; Histone-deacetylase-inhibitor; Synergist][Indication:Histone deacetylase inhibitor; Apoptosis stimulator; Cytochrome P450 2D6 inhibitor highest ph ...] 0.01474 0.00391000000000000030.011780000000000001 <0.001221 0.00308500000000000010.018120000000000001⋯<0.001221 <0.001221 0.00198100000000000010.00596999999999999960.00185699999999999990.00834999999999999980.03048 <0.001221 <0.001641 0.0024450000000000001
GNF-00-0558-2696-5Erlotinib Tarceva [Erlotinib(in Wikipedia),Tarceva][CAS:183321-74-6,183319-69-9][MOA:Protein Kinase Inhibitors][Indication:For the treatment of patients with locally advanced or metastatic non-small cell lung cancer after failure of at least one prior chemotherap ...] 7.1399999999999997 >20 17.539999999999999 15.220000000000001 0.036299999999999999 >20 ⋯1.609 11.789999999999999 1.514 >20 4.79 >20 1.1759999999999999 1.4139999999999999 >20 >20
GNF-00-0558-2805-2PD-0332991 NA [Pd-0332991,6-acetyl-8-cyclopentyl-5-methyl-2-(5-piperazin-1-ylpyridin-2-ylamino)-8H-pyrido(2,3-d)pyrimidin-7-one][MOA:Cyclin-dependent-kinase-4-inhibitor; Cyclin-dependent-kinase-6-inhibitor; Cyclin-dependent-kinase-inhibitor; Cytostatic][Indic ...] >20 1.962 0.753 0.093299999999999994 2.4249999999999998 >20 ⋯1.4910000000000001 0.58099999999999996 1.9670000000000001 11.029999999999999 4.4000000000000004 0.74299999999999999 >20 0.57899999999999996 NA 0.63700000000000001
GNF-00-0558-2806-3Lapatinib Tykerb [Lapatinib(in Wikipedia),Tycerb][CAS:388082-78-8,231277-92-2][MOA:Antineoplastic Agents; Protein Kinase Inhibitors][Indication:Indicated in combination with capecitabine for the treatment of patients with advanced or metastatic breast cancer who ...] >20 >20 8.0700000000000003 >20 0.35699999999999998 >20 ⋯>20 >20 11.68 >20 13.43 10.83 >20 >20 >20 >20
GNF-00-0558-2811-0LBW242 NA [lbw-242][MOA:Cytostatic; Smac-protein-stimulator][Target:BIR3 domain of X-linked IAP, X-linked IAP][GNF Document:CB1\Chen_Julian-Pyridones as Novel IAP Antagonists.pdf][Patent:Organic compounds(2005); Process for preparing dipeptide amides(2006 ...] >20 >20 >20 >20 >20 >20 ⋯>20 >20 >20 >20 >20 >20 >20 >20 >20 >20
GNF-00-0558-2817-6RAF265 NA [chir-265][MOA:Angiogenesis-inhibitor; Cytostatic][Indication:VEGFR inhibitor; Raf inhibitor highest phase:Phase 1 - Skin melanoma, Cancer][Target:Raf kinase, Raf][GNF Document:B-Raf\RAF Kinase LN review-Tommasi-v2.doc, B-Raf\Raf Core Team 05090 ...]>20 12.08 5.6100000000000003 0.045199999999999997 3.0369999999999999 19.98 ⋯>20 1.7849999999999999 6.04 >20 13.1 0.15870000000000001 >20 0.014760000000000001 >20 6.7699999999999996
GNF-00-0558-2820-1TKI258 Dovitinib [CAS:405169-16-6][Indication:FLT3 inhibitor; KIT inhibitor; VEGFR antagonist; FGFR antagonist highest phase:Phase 1 - Acute myelogenous leukemia, Multiple myeloma, Solid tumor][Target:Vegfr, Fibroblast growth factor receptor 3, Protein tyrosine ...] >20 2.9569999999999999 2.8919999999999999 0.21690000000000001 3.4500000000000002 2.23 ⋯2.706 0.71699999999999997 1.0529999999999999 >20 4.5800000000000001 6.2800000000000002 19.48 1.9139999999999999 >20 >20
GNF-00-0558-2834-7Vandetanib Zactima [Vandetanib(in Wikipedia),N-(4-bromo-2-fluorophenyl)-6-methoxy-7-((1-methylpiperidin-4-yl)methoxy)quinazolin-4-amine][CAS:443913-73-3,338992-00-0][MOA:Angiogenesis-inhibitor; Apoptosis-inducer; Cytostatic; Egf-antagonist; Epidermal-growth-factor ...] 17.289999999999999 10.050000000000001 11.789999999999999 3.4399999999999999 1.024 >20 ⋯14.25 14.99 5.1299999999999999 >20 16.079999999999998 9.6500000000000004 13.77 9.6899999999999995 9.0199999999999996 >20
GNF-00-0558-2842-717-AAG Tanespimycin[17-N-Allylamino-17-demethoxygeldanamycin(in Wikipedia),Tanespimycin][CAS:75747-14-7,64202-81-9][MOA:Antibiotic; Apoptosis-inducer; Cytostatic; Hsp-90-inhibitor; Prodrug of geldanamycin; Synergist; Tyrosine-kinase-inhibitor; Androgen-antagonist; ...] 0.032800000000000003 0.442 0.074800000000000005 0.01242 0.0407 0.059400000000000001⋯0.050500000000000003 0.36899999999999999 0.056500000000000002 0.0385 0.037400000000000003 0.015259999999999999 0.14149999999999999 0.01192 0.0345000000000000030.0344
GNF-00-0558-2844-9PD-0325901 NA [Pd-0325901,Pd 0325901][CAS:391210-10-9][MOA:Apoptosis-inducer; Cytostatic; Map-kinase-inhibitor; Mek-1-protein-kinase-inhibitor][Indication:MEK inhibitor; Immunosuppressant highest phase:Phase 2 - Breast tumor, Colon tumor, Melanoma, Non-small- ...] >20 >20 NoFit 0.24399999999999999 0.0047600000000000003>20 ⋯<0.001244 >20 3.9199999999999999 NoFit >20 NoFit >20 <0.001221 5.1900000000000004 >20
GNF-00-0558-3160-2AZD0530 Saracatinib [Saracatinib,N-(5-chloro-1,3-benzodioxol-4-yl)-7-(2-(4-methylpiperazin-1-yl)ethoxy)-5-(tetrahydro-2H-pyran-4-yloxy)quinazolin-4-amine][MOA:Abl-tyrosine-kinase-inhibitor; Apoptosis-inducer; Cytostatic; Estrogen-receptor-alpha-antagonist; Src-tyro ...] >20 13.300000000000001 >20 5.1200000000000001 0.75600000000000001 >20 ⋯>20 >20 0.72299999999999998 >20 >20 >20 0.80500000000000005 12.83 12.67 7.2000000000000002
In [259]:
select_cl <- CCLE_drug %>% 
    filter(CCLE_drug$Target == 'EGFR') %>% 
    dplyr::select('CCLE Cell Line Name') %>% 
    t() %>% c()
In [260]:
CCLE_exp %>% dplyr::select(c('Description',select_cl))
Warning message:
“Using an external vector in selections was deprecated in tidyselect 1.1.0.
ℹ Please use `all_of()` or `any_of()` instead.
  # Was:
  data %>% select(select_cl)

  # Now:
  data %>% select(all_of(select_cl))

See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.”
Error in `dplyr::select()`:
! Can't subset columns that don't exist.
✖ Columns `1321N1_CENTRAL_NERVOUS_SYSTEM`, `AZ521_STOMACH`, `BGC823_STOMACH`, `C3A_LIVER`, `CHL1_SKIN`, etc. don't exist.
Traceback:

1. CCLE_exp %>% dplyr::select(c("Description", select_cl))
2. dplyr::select(., c("Description", select_cl))
3. select.data.frame(., c("Description", select_cl))
4. tidyselect::eval_select(expr(c(...)), data = .data, error_call = error_call)
5. eval_select_impl(data, names(data), as_quosure(expr, env), include = include, 
 .     exclude = exclude, strict = strict, name_spec = name_spec, 
 .     allow_rename = allow_rename, allow_empty = allow_empty, allow_predicates = allow_predicates, 
 .     error_call = error_call, )
6. with_subscript_errors(out <- vars_select_eval(vars, expr, strict = strict, 
 .     data = x, name_spec = name_spec, uniquely_named = uniquely_named, 
 .     allow_rename = allow_rename, allow_empty = allow_empty, allow_predicates = allow_predicates, 
 .     type = type, error_call = error_call), type = type)
7. try_fetch(expr, vctrs_error_subscript = function(cnd) {
 .     cnd$subscript_action <- subscript_action(type)
 .     cnd$subscript_elt <- "column"
 .     cnd_signal(cnd)
 . })
8. withCallingHandlers(expr, condition = function(cnd) {
 .     {
 .         .__handler_frame__. <- TRUE
 .         .__setup_frame__. <- frame
 .         if (inherits(cnd, "message")) {
 .             except <- c("warning", "error")
 .         }
 .         else if (inherits(cnd, "warning")) {
 .             except <- "error"
 .         }
 .         else {
 .             except <- ""
 .         }
 .     }
 .     while (!is_null(cnd)) {
 .         if (inherits(cnd, "vctrs_error_subscript")) {
 .             out <- handlers[[1L]](cnd)
 .             if (!inherits(out, "rlang_zap")) 
 .                 throw(out)
 .         }
 .         inherit <- .subset2(.subset2(cnd, "rlang"), "inherit")
 .         if (is_false(inherit)) {
 .             return()
 .         }
 .         cnd <- .subset2(cnd, "parent")
 .     }
 . })
9. vars_select_eval(vars, expr, strict = strict, data = x, name_spec = name_spec, 
 .     uniquely_named = uniquely_named, allow_rename = allow_rename, 
 .     allow_empty = allow_empty, allow_predicates = allow_predicates, 
 .     type = type, error_call = error_call)
10. walk_data_tree(expr, data_mask, context_mask)
11. eval_c(expr, data_mask, context_mask)
12. reduce_sels(node, data_mask, context_mask, init = init)
13. walk_data_tree(new, data_mask, context_mask)
14. eval_c(expr, data_mask, context_mask)
15. reduce_sels(node, data_mask, context_mask, init = init)
16. walk_data_tree(new, data_mask, context_mask)
17. as_indices_sel_impl(out, vars = vars, strict = strict, data = data, 
  .     allow_predicates = allow_predicates, call = error_call, arg = as_label(expr))
18. as_indices_impl(x, vars, call = call, arg = arg, strict = strict)
19. chr_as_locations(x, vars, call = call, arg = arg)
20. vctrs::vec_as_location(x, n = length(vars), names = vars, call = call, 
  .     arg = arg)
21. (function () 
  . stop_subscript_oob(i = i, subscript_type = subscript_type, names = names, 
  .     subscript_action = subscript_action, subscript_arg = subscript_arg, 
  .     call = call))()
22. stop_subscript_oob(i = i, subscript_type = subscript_type, names = names, 
  .     subscript_action = subscript_action, subscript_arg = subscript_arg, 
  .     call = call)
23. stop_subscript(class = "vctrs_error_subscript_oob", i = i, subscript_type = subscript_type, 
  .     ..., call = call)
24. abort(class = c(class, "vctrs_error_subscript"), i = i, ..., 
  .     call = call)
25. signal_abort(cnd, .file)
26. signalCondition(cnd)
27. (function (cnd) 
  . {
  .     {
  .         .__handler_frame__. <- TRUE
  .         .__setup_frame__. <- frame
  .         if (inherits(cnd, "message")) {
  .             except <- c("warning", "error")
  .         }
  .         else if (inherits(cnd, "warning")) {
  .             except <- "error"
  .         }
  .         else {
  .             except <- ""
  .         }
  .     }
  .     while (!is_null(cnd)) {
  .         if (inherits(cnd, "vctrs_error_subscript")) {
  .             out <- handlers[[1L]](cnd)
  .             if (!inherits(out, "rlang_zap")) 
  .                 throw(out)
  .         }
  .         inherit <- .subset2(.subset2(cnd, "rlang"), "inherit")
  .         if (is_false(inherit)) {
  .             return()
  .         }
  .         cnd <- .subset2(cnd, "parent")
  .     }
  . })(structure(list(message = "", trace = structure(list(call = list(
  .     IRkernel::main(), kernel$run(), handle_shell(), executor$execute(msg), 
  .     tryCatch(evaluate(request$content$code, envir = .GlobalEnv, 
  .         output_handler = oh, stop_on_error = 1L), interrupt = function(cond) {
  .         log_debug("Interrupt during execution")
  .         interrupted <<- TRUE
  .     }, error = .self$handle_error), tryCatchList(expr, classes, 
  .         parentenv, handlers), tryCatchOne(tryCatchList(expr, 
  .         names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, 
  .         handlers[[nh]]), doTryCatch(return(expr), name, parentenv, 
  .         handler), tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), 
  .     tryCatchOne(expr, names, parentenv, handlers[[1L]]), doTryCatch(return(expr), 
  .         name, parentenv, handler), evaluate(request$content$code, 
  .         envir = .GlobalEnv, output_handler = oh, stop_on_error = 1L), 
  .     evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos, 
  .         debug = debug, last = i == length(out), use_try = stop_on_error != 
  .             2L, keep_warning = keep_warning, keep_message = keep_message, 
  .         log_echo = log_echo, log_warning = log_warning, output_handler = output_handler, 
  .         include_timing = include_timing), timing_fn(handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, 
  .         envir, enclos, user_handlers)), warning = wHandler, error = eHandler, 
  .         message = mHandler))), handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, 
  .         envir, enclos, user_handlers)), warning = wHandler, error = eHandler, 
  .         message = mHandler)), try(f, silent = TRUE), tryCatch(expr, 
  .         error = function(e) {
  .             call <- conditionCall(e)
  .             if (!is.null(call)) {
  .                 if (identical(call[[1L]], quote(doTryCatch))) 
  .                   call <- sys.call(-4L)
  .                 dcall <- deparse(call, nlines = 1L)
  .                 prefix <- paste("Error in", dcall, ": ")
  .                 LONG <- 75L
  .                 sm <- strsplit(conditionMessage(e), "\n")[[1L]]
  .                 w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], 
  .                   type = "w")
  .                 if (is.na(w)) 
  .                   w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], 
  .                     type = "b")
  .                 if (w > LONG) 
  .                   prefix <- paste0(prefix, "\n  ")
  .             }
  .             else prefix <- "Error : "
  .             msg <- paste0(prefix, conditionMessage(e), "\n")
  .             .Internal(seterrmessage(msg[1L]))
  .             if (!silent && isTRUE(getOption("show.error.messages"))) {
  .                 cat(msg, file = outFile)
  .                 .Internal(printDeferredWarnings())
  .             }
  .             invisible(structure(msg, class = "try-error", condition = e))
  .         }), tryCatchList(expr, classes, parentenv, handlers), 
  .     tryCatchOne(expr, names, parentenv, handlers[[1L]]), doTryCatch(return(expr), 
  .         name, parentenv, handler), withCallingHandlers(withVisible(eval_with_user_handlers(expr, 
  .         envir, enclos, user_handlers)), warning = wHandler, error = eHandler, 
  .         message = mHandler), withVisible(eval_with_user_handlers(expr, 
  .         envir, enclos, user_handlers)), eval_with_user_handlers(expr, 
  .         envir, enclos, user_handlers), eval(expr, envir, enclos), 
  .     eval(expr, envir, enclos), CCLE_exp %>% dplyr::select(c("Description", 
  .         select_cl)), dplyr::select(., c("Description", select_cl)), 
  .     select.data.frame(., c("Description", select_cl)), tidyselect::eval_select(expr(c(...)), 
  .         data = .data, error_call = error_call), eval_select_impl(data, 
  .         names(data), as_quosure(expr, env), include = include, 
  .         exclude = exclude, strict = strict, name_spec = name_spec, 
  .         allow_rename = allow_rename, allow_empty = allow_empty, 
  .         allow_predicates = allow_predicates, error_call = error_call, 
  .         ), with_subscript_errors(out <- vars_select_eval(vars, 
  .         expr, strict = strict, data = x, name_spec = name_spec, 
  .         uniquely_named = uniquely_named, allow_rename = allow_rename, 
  .         allow_empty = allow_empty, allow_predicates = allow_predicates, 
  .         type = type, error_call = error_call), type = type), 
  .     try_fetch(expr, vctrs_error_subscript = function(cnd) {
  .         cnd$subscript_action <- subscript_action(type)
  .         cnd$subscript_elt <- "column"
  .         cnd_signal(cnd)
  .     }), withCallingHandlers(expr, condition = function(cnd) {
  .         {
  .             .__handler_frame__. <- TRUE
  .             .__setup_frame__. <- frame
  .             if (inherits(cnd, "message")) {
  .                 except <- c("warning", "error")
  .             }
  .             else if (inherits(cnd, "warning")) {
  .                 except <- "error"
  .             }
  .             else {
  .                 except <- ""
  .             }
  .         }
  .         while (!is_null(cnd)) {
  .             if (inherits(cnd, "vctrs_error_subscript")) {
  .                 out <- handlers[[1L]](cnd)
  .                 if (!inherits(out, "rlang_zap")) 
  .                   throw(out)
  .             }
  .             inherit <- .subset2(.subset2(cnd, "rlang"), "inherit")
  .             if (is_false(inherit)) {
  .                 return()
  .             }
  .             cnd <- .subset2(cnd, "parent")
  .         }
  .     }), vars_select_eval(vars, expr, strict = strict, data = x, 
  .         name_spec = name_spec, uniquely_named = uniquely_named, 
  .         allow_rename = allow_rename, allow_empty = allow_empty, 
  .         allow_predicates = allow_predicates, type = type, error_call = error_call), 
  .     walk_data_tree(expr, data_mask, context_mask), eval_c(expr, 
  .         data_mask, context_mask), reduce_sels(node, data_mask, 
  .         context_mask, init = init), walk_data_tree(new, data_mask, 
  .         context_mask), eval_c(expr, data_mask, context_mask), 
  .     reduce_sels(node, data_mask, context_mask, init = init), 
  .     walk_data_tree(new, data_mask, context_mask), as_indices_sel_impl(out, 
  .         vars = vars, strict = strict, data = data, allow_predicates = allow_predicates, 
  .         call = error_call, arg = as_label(expr)), as_indices_impl(x, 
  .         vars, call = call, arg = arg, strict = strict), chr_as_locations(x, 
  .         vars, call = call, arg = arg), vctrs::vec_as_location(x, 
  .         n = length(vars), names = vars, call = call, arg = arg), 
  .     `<fn>`(), stop_subscript_oob(i = i, subscript_type = subscript_type, 
  .         names = names, subscript_action = subscript_action, subscript_arg = subscript_arg, 
  .         call = call), stop_subscript(class = "vctrs_error_subscript_oob", 
  .         i = i, subscript_type = subscript_type, ..., call = call), 
  .     abort(class = c(class, "vctrs_error_subscript"), i = i, ..., 
  .         call = call)), parent = c(0L, 1L, 2L, 3L, 4L, 5L, 6L, 
  . 7L, 6L, 9L, 10L, 4L, 12L, 13L, 13L, 15L, 16L, 17L, 18L, 19L, 
  . 13L, 13L, 13L, 23L, 24L, 0L, 0L, 0L, 28L, 29L, 30L, 31L, 32L, 
  . 30L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 0L, 
  . 46L, 47L, 48L), visible = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, 
  . TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, 
  . TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, 
  . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  . FALSE, FALSE, FALSE), namespace = c("IRkernel", NA, "IRkernel", 
  . NA, "base", "base", "base", "base", "base", "base", "base", "evaluate", 
  . "evaluate", "evaluate", "evaluate", "base", "base", "base", "base", 
  . "base", "base", "base", "evaluate", "base", "base", NA, "dplyr", 
  . "dplyr", "tidyselect", "tidyselect", "tidyselect", "rlang", "base", 
  . "tidyselect", "tidyselect", "tidyselect", "tidyselect", "tidyselect", 
  . "tidyselect", "tidyselect", "tidyselect", "tidyselect", "tidyselect", 
  . "tidyselect", "vctrs", "vctrs", "vctrs", "vctrs", "rlang"), scope = c("::", 
  . NA, "local", NA, "::", "local", "local", "local", "local", "local", 
  . "local", "::", ":::", "local", "local", "::", "::", "local", 
  . "local", "local", "::", "::", ":::", "::", "::", NA, "::", ":::", 
  . "::", ":::", ":::", "::", "::", ":::", ":::", ":::", ":::", ":::", 
  . ":::", ":::", ":::", ":::", ":::", ":::", "::", "local", ":::", 
  . ":::", "::"), error_frame = c(FALSE, FALSE, FALSE, FALSE, FALSE, 
  . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  . FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, 
  . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)), row.names = c(NA, 
  . -49L), version = 2L, class = c("rlang_trace", "rlib_trace", "tbl", 
  . "data.frame")), parent = NULL, i = c("1321N1_CENTRAL_NERVOUS_SYSTEM", 
  . "22RV1_PROSTATE", "42MGBA_CENTRAL_NERVOUS_SYSTEM", "5637_URINARY_TRACT", 
  . "639V_URINARY_TRACT", "697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "769P_KIDNEY", "786O_KIDNEY", "8305C_THYROID", "8505C_THYROID", 
  . "8MGBA_CENTRAL_NERVOUS_SYSTEM", "A172_CENTRAL_NERVOUS_SYSTEM", 
  . "A204_SOFT_TISSUE", "A2058_SKIN", "A253_SALIVARY_GLAND", "A2780_OVARY", 
  . "A375_SKIN", "A549_LUNG", "A673_BONE", "ACHN_KIDNEY", "ALLSIL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "AMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "AN3CA_ENDOMETRIUM", 
  . "ASPC1_PANCREAS", "AU565_BREAST", "AZ521_STOMACH", "BCPAP_THYROID", 
  . "BDCM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BFTC909_KIDNEY", 
  . "BGC823_STOMACH", "BL41_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "BL70_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BT20_BREAST", "BT474_BREAST", 
  . "BT549_BREAST", "BXPC3_PANCREAS", "C2BBE1_LARGE_INTESTINE", "C32_SKIN", 
  . "C3A_LIVER", "CAKI2_KIDNEY", "CAL12T_LUNG", "CAL27_UPPER_AERODIGESTIVE_TRACT", 
  . "CAL78_BONE", "CAL851_BREAST", "CALU1_LUNG", "CALU3_LUNG", "CALU6_LUNG", 
  . "CAMA1_BREAST", "CAPAN2_PANCREAS", "CAS1_CENTRAL_NERVOUS_SYSTEM", 
  . "CCK81_LARGE_INTESTINE", "CHL1_SKIN", "CHP212_AUTONOMIC_GANGLIA", 
  . "CI1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "CMK86_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "COLO201_LARGE_INTESTINE", "COLO205_LARGE_INTESTINE", "COLO320_LARGE_INTESTINE", 
  . "COLO677_LUNG", "COLO678_LARGE_INTESTINE", "COLO679_SKIN", "COLO699_LUNG", 
  . "COLO741_SKIN", "CORL105_LUNG", "CORL23_LUNG", "COV318_OVARY", 
  . "COV504_OVARY", "DAOY_CENTRAL_NERVOUS_SYSTEM", "DBTRG05MG_CENTRAL_NERVOUS_SYSTEM", 
  . "DEL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "DETROIT562_UPPER_AERODIGESTIVE_TRACT", 
  . "DKMG_CENTRAL_NERVOUS_SYSTEM", "DMS114_LUNG", "DOHH2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "DOV13_OVARY", "DU145_PROSTATE", "DV90_LUNG", "EB1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "EB2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "EBC1_LUNG", "EFE184_ENDOMETRIUM", 
  . "EFM19_BREAST", "EFO21_OVARY", "EFO27_OVARY", "EM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "EN_ENDOMETRIUM", "ES2_OVARY", "ESS1_ENDOMETRIUM", "F36P_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "FADU_UPPER_AERODIGESTIVE_TRACT", "FU97_STOMACH", "FUOV1_OVARY", 
  . "G361_SKIN", "G401_SOFT_TISSUE", "G402_SOFT_TISSUE", "GAMG_CENTRAL_NERVOUS_SYSTEM", 
  . "GB1_CENTRAL_NERVOUS_SYSTEM", "GCIY_STOMACH", "GCT_SOFT_TISSUE", 
  . "GI1_CENTRAL_NERVOUS_SYSTEM", "GLC82_LUNG", "GMS10_CENTRAL_NERVOUS_SYSTEM", 
  . "GRANTA519_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "H4_CENTRAL_NERVOUS_SYSTEM", 
  . "HARA_LUNG", "HCC1187_BREAST", "HCC1395_BREAST", "HCC15_LUNG", 
  . "HCC1569_BREAST", "HCC1806_BREAST", "HCC1954_BREAST", "HCC2935_LUNG", 
  . "HCC4006_LUNG", "HCC44_LUNG", "HCC56_LARGE_INTESTINE", "HCC70_BREAST", 
  . "HCC78_LUNG", "HCC827_LUNG", "HCT116_LARGE_INTESTINE", "HCT15_LARGE_INTESTINE", 
  . "HDMYZ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HDQP1_BREAST", "HEC151_ENDOMETRIUM", 
  . "HEC1A_ENDOMETRIUM", "HEC1B_ENDOMETRIUM", "HEC251_ENDOMETRIUM", 
  . "HEC265_ENDOMETRIUM", "HEC59_ENDOMETRIUM", "HEC6_ENDOMETRIUM", 
  . "HEL9217_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HEP3B217_LIVER", 
  . "HEPG2_LIVER", "HEYA8_OVARY", "HGC27_STOMACH", "HH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "HLE_LIVER", "HLF_LIVER", "HMC18_BREAST", "HMCB_SKIN", "HOS_BONE", 
  . "HPAC_PANCREAS", "HPAFII_PANCREAS", "HS229T_LUNG", "HS294T_SKIN", 
  . "HS578T_BREAST", "HS683_CENTRAL_NERVOUS_SYSTEM", "HS695T_SKIN", 
  . "HS729_SOFT_TISSUE", "HS739T_BREAST", "HS746T_STOMACH", "HS766T_PANCREAS", 
  . "HS840T_UPPER_AERODIGESTIVE_TRACT", "HS852T_SKIN", "HS895T_SKIN", 
  . "HS936T_SKIN", "HS939T_SKIN", "HS944T_SKIN", "HSC2_UPPER_AERODIGESTIVE_TRACT", 
  . "HT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HT1080_SOFT_TISSUE", 
  . "HT1197_URINARY_TRACT", "HT1376_URINARY_TRACT", "HT144_SKIN", 
  . "HT29_LARGE_INTESTINE", "HUCCT1_BILIARY_TRACT", "HUH1_LIVER", 
  . "HUPT3_PANCREAS", "HUPT4_PANCREAS", "HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "IALM_LUNG", "IGR37_SKIN", "IGR39_SKIN", "IGROV1_OVARY", "IM95_STOMACH", 
  . "IMR32_AUTONOMIC_GANGLIA", "IPC298_SKIN", "ISHIKAWAHERAKLIO02ER_ENDOMETRIUM", 
  . "ISTMES1_PLEURA", "ISTMES2_PLEURA", "J82_URINARY_TRACT", "JHH2_LIVER", 
  . "JHH4_LIVER", "JHH5_LIVER", "JHH6_LIVER", "JHH7_LIVER", "JHOS2_OVARY", 
  . "JHOS4_OVARY", "JHUEM2_ENDOMETRIUM", "JM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "JMSU1_URINARY_TRACT", "JURKAT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "JVM3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "K029AX_SKIN", "KALS1_CENTRAL_NERVOUS_SYSTEM", 
  . "KARPAS299_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KARPAS422_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KARPAS620_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KASUMI2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KCIMOH1_PANCREAS", "KCL22_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KE39_STOMACH", "KE97_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KELLY_AUTONOMIC_GANGLIA", 
  . "KG1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KHM1B_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KLE_ENDOMETRIUM", "KMBC2_URINARY_TRACT", "KMM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KMRC1_KIDNEY", "KMRC2_KIDNEY", "KMS11_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KMS12BM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS26_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KMS34_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KNS42_CENTRAL_NERVOUS_SYSTEM", 
  . "KNS60_CENTRAL_NERVOUS_SYSTEM", "KNS62_LUNG", "KNS81_CENTRAL_NERVOUS_SYSTEM", 
  . "KO52_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KP1N_PANCREAS", "KP1NL_PANCREAS", 
  . "KP2_PANCREAS", "KP3_PANCREAS", "KP4_PANCREAS", "KPNSI9S_AUTONOMIC_GANGLIA", 
  . "KU812_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KURAMOCHI_OVARY", 
  . "KYM1_SOFT_TISSUE", "KYSE140_OESOPHAGUS", "KYSE150_OESOPHAGUS", 
  . "KYSE180_OESOPHAGUS", "KYSE30_OESOPHAGUS", "KYSE410_OESOPHAGUS", 
  . "KYSE450_OESOPHAGUS", "KYSE510_OESOPHAGUS", "KYSE520_OESOPHAGUS", 
  . "KYSE70_OESOPHAGUS", "L33_PANCREAS", "L363_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "L428_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "LC1SQSF_LUNG", "LCLC103H_LUNG", 
  . "LN18_CENTRAL_NERVOUS_SYSTEM", "LN229_CENTRAL_NERVOUS_SYSTEM", 
  . "LOUNH91_LUNG", "LOXIMVI_SKIN", "LP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "LS123_LARGE_INTESTINE", "LS411N_LARGE_INTESTINE", "LS513_LARGE_INTESTINE", 
  . "LU99_LUNG", "LUDLU1_LUNG", "M059J", "MALME3M_SKIN", "MB157_BREAST", 
  . "MC116_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MCAS_OVARY", "MCF7_BREAST", 
  . "MDAMB157_BREAST", "MDAMB175VII_BREAST", "MDAMB415_BREAST", "MDAMB435S_SKIN", 
  . "MDAMB436_BREAST", "MDAMB453_BREAST", "MDAMB468_BREAST", "MEC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "MEG01_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MELHO_SKIN", "MESSA_SOFT_TISSUE", 
  . "MEWO_SKIN", "MFE280_ENDOMETRIUM", "MFE296_ENDOMETRIUM", "MFE319_ENDOMETRIUM", 
  . "MG63_BONE", "MHHES1_BONE", "MIAPACA2_PANCREAS", "MINO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "MJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MKN45_STOMACH", "MKN7_STOMACH", 
  . "MKN74_STOMACH", "MOGGCCM_CENTRAL_NERVOUS_SYSTEM", "MOLP8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "MOLT16_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MONOMAC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "MORCPR_LUNG", "MPP89_PLEURA", "MSTO211H_PLEURA", "NCIH1048_LUNG", 
  . "NCIH1092_LUNG", "NCIH1155_LUNG", "NCIH1184_LUNG", "NCIH1299_LUNG", 
  . "NCIH1339_LUNG", "NCIH1341_LUNG", "NCIH1355_LUNG", "NCIH1373_LUNG", 
  . "NCIH1563_LUNG", "NCIH1568_LUNG", "NCIH1573_LUNG", "NCIH1581_LUNG", 
  . "NCIH1648_LUNG", "NCIH1650_LUNG", "NCIH1651_LUNG", "NCIH1666_LUNG", 
  . "NCIH1693_LUNG", "NCIH1694_LUNG", "NCIH1703_LUNG", "NCIH1792_LUNG", 
  . "NCIH1793_LUNG", "NCIH1869_LUNG", "NCIH1915_LUNG", "NCIH1944_LUNG", 
  . "NCIH1975_LUNG", "NCIH2009_LUNG", "NCIH2023_LUNG", "NCIH2030_LUNG", 
  . "NCIH2052_PLEURA", "NCIH2085_LUNG", "NCIH2087_LUNG", "NCIH211_LUNG", 
  . "NCIH2122_LUNG", "NCIH2170_LUNG", "NCIH2172_LUNG", "NCIH2228_LUNG", 
  . "NCIH226_LUNG", "NCIH2286_LUNG", "NCIH23_LUNG", "NCIH2444_LUNG", 
  . "NCIH2452_PLEURA", "NCIH28_PLEURA", "NCIH322_LUNG", "NCIH3255_LUNG", 
  . "NCIH358_LUNG", "NCIH441_LUNG", "NCIH460_LUNG", "NCIH520_LUNG", 
  . "NCIH522_LUNG", "NCIH647_LUNG", "NCIH650_LUNG", "NCIH661_LUNG", 
  . "NCIH727_LUNG", "NCIH747_LARGE_INTESTINE", "NCIH810_LUNG", "NCIN87_STOMACH", 
  . "NCO2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "NIHOVCAR3_OVARY", 
  . "NUGC3_STOMACH", "NUGC4_STOMACH", "OC314_OVARY", "OC315_OVARY", 
  . "OC316_OVARY", "OCIAML2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "OCIAML5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCILY10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "OCUM1_STOMACH", "OE21_OESOPHAGUS", "OE33_OESOPHAGUS", "ONCODG1_OVARY", 
  . "ONS76_CENTRAL_NERVOUS_SYSTEM", "OPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "OV90_OVARY", "OVCAR4_OVARY", "OVCAR8_OVARY", "OVMANA_OVARY", 
  . "OVSAHO_OVARY", "OVTOKO_OVARY", "P12ICHIKAWA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "P31FUJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "PANC0203_PANCREAS", "PANC0327_PANCREAS", "PANC0403_PANCREAS", 
  . "PANC1005_PANCREAS", "PATU8902_PANCREAS", "PC14_LUNG", "PC3_PROSTATE", 
  . "PFEIFFER_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "PK1_PANCREAS", 
  . "PK45H_PANCREAS", "PK59_PANCREAS", "PL45_PANCREAS", "PLCPRF5_LIVER", 
  . "PSN1_PANCREAS", "QGP1_PANCREAS", "RAJI_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "RD_SOFT_TISSUE", "REH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "RERFGC1B_STOMACH", 
  . "RERFLCAI_LUNG", "RERFLCMS_LUNG", "RKN_OVARY", "RKO_LARGE_INTESTINE", 
  . "RL952_ENDOMETRIUM", "RPMI7951_SKIN", "RPMI8402_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "RT112_URINARY_TRACT", "RT4_URINARY_TRACT", "RVH421_SKIN", "SAOS2_BONE", 
  . "SBC5_LUNG", "SCABER_URINARY_TRACT", "SCC25_UPPER_AERODIGESTIVE_TRACT", 
  . "SCC9_UPPER_AERODIGESTIVE_TRACT", "SF126_CENTRAL_NERVOUS_SYSTEM", 
  . "SF295_CENTRAL_NERVOUS_SYSTEM", "SF8657", "SH10TC_STOMACH", "SHP77_LUNG", 
  . "SIGM5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SIMA_AUTONOMIC_GANGLIA", 
  . "SJRH30_SOFT_TISSUE", "SJSA1_BONE", "SKBR3_BREAST", "SKCO1_LARGE_INTESTINE", 
  . "SKES1_BONE", "SKHEP1_LIVER", "SKLMS1_SOFT_TISSUE", "SKLU1_LUNG", 
  . "SKMEL2_SKIN", "SKMEL24_SKIN", "SKMEL30_SKIN", "SKMEL31_SKIN", 
  . "SKMEL5_SKIN", "SKMES1_LUNG", "SKMM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "SKNAS_AUTONOMIC_GANGLIA", "SKNBE2_AUTONOMIC_GANGLIA", "SKNDZ_AUTONOMIC_GANGLIA", 
  . "SKNFI_AUTONOMIC_GANGLIA", "SKNSH_AUTONOMIC_GANGLIA", "SKOV3_OVARY", 
  . "SNGM_ENDOMETRIUM", "SNU1_STOMACH", "SNU16_STOMACH", "SNU182_LIVER", 
  . "SNU387_LIVER", "SNU398_LIVER", "SNU423_LIVER", "SNU449_LIVER", 
  . "SNU475_LIVER", "SNUC2A_LARGE_INTESTINE", "SNUC2B", "SQ1_LUNG", 
  . "SU8686_PANCREAS", "SUDHL10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "SUDHL4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUDHL6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "SUDHL8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUIT2_PANCREAS", 
  . "SUPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUPT1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "SW1088_CENTRAL_NERVOUS_SYSTEM", "SW1271_LUNG", "SW1353_BONE", 
  . "SW1417_LARGE_INTESTINE", "SW1573_LUNG", "SW1990_PANCREAS", "SW403_LARGE_INTESTINE", 
  . "SW48_LARGE_INTESTINE", "SW480_LARGE_INTESTINE", "SW579_THYROID", 
  . "SW620_LARGE_INTESTINE", "SW780_URINARY_TRACT", "SW900_LUNG", 
  . "T24_URINARY_TRACT", "T3M10_LUNG", "T47D_BREAST", "T84_LARGE_INTESTINE", 
  . "T98G_CENTRAL_NERVOUS_SYSTEM", "TC71_BONE", "TCCSUP_URINARY_TRACT", 
  . "TE1_OESOPHAGUS", "TE11_OESOPHAGUS", "TE15_OESOPHAGUS", "TE5_OESOPHAGUS", 
  . "TE617T_SOFT_TISSUE", "TE9_OESOPHAGUS", "TEN_ENDOMETRIUM", "TOLEDO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "TOV112D_OVARY", "TOV21G_OVARY", "TT2609C02_THYROID", "TYKNU_OVARY", 
  . "U118MG_CENTRAL_NERVOUS_SYSTEM", "U2OS_BONE", "U87MG_CENTRAL_NERVOUS_SYSTEM", 
  . "U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "UACC257_SKIN", "UACC62_SKIN", 
  . "UACC812_BREAST", "UMUC3_URINARY_TRACT", "VMRCLCD_LUNG", "VMRCRCW_KIDNEY", 
  . "VMRCRCZ_KIDNEY", "WM115_SKIN", "WM1799_SKIN", "WM2664_SKIN", 
  . "WM793_SKIN", "WM88_SKIN", "WM983B_SKIN", "YKG1_CENTRAL_NERVOUS_SYSTEM", 
  . "ZR751_BREAST", "ZR7530_BREAST", "1321N1_CENTRAL_NERVOUS_SYSTEM", 
  . "22RV1_PROSTATE", "42MGBA_CENTRAL_NERVOUS_SYSTEM", "5637_URINARY_TRACT", 
  . "639V_URINARY_TRACT", "697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "769P_KIDNEY", "8305C_THYROID", "8505C_THYROID", "8MGBA_CENTRAL_NERVOUS_SYSTEM", 
  . "A172_CENTRAL_NERVOUS_SYSTEM", "A204_SOFT_TISSUE", "A2058_SKIN", 
  . "A253_SALIVARY_GLAND", "A2780_OVARY", "A375_SKIN", "A549_LUNG", 
  . "A673_BONE", "ACHN_KIDNEY", "ALLSIL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "AMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "AN3CA_ENDOMETRIUM", 
  . "ASPC1_PANCREAS", "AU565_BREAST", "AZ521_STOMACH", "BCPAP_THYROID", 
  . "BDCM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BFTC909_KIDNEY", 
  . "BGC823_STOMACH", "BL41_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "BL70_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BT20_BREAST", "BT474_BREAST", 
  . "BT549_BREAST", "BXPC3_PANCREAS", "C2BBE1_LARGE_INTESTINE", "C32_SKIN", 
  . "C3A_LIVER", "CAKI2_KIDNEY", "CAL12T_LUNG", "CAL27_UPPER_AERODIGESTIVE_TRACT", 
  . "CAL78_BONE", "CAL851_BREAST", "CALU1_LUNG", "CALU3_LUNG", "CALU6_LUNG", 
  . "CAMA1_BREAST", "CAPAN2_PANCREAS", "CAS1_CENTRAL_NERVOUS_SYSTEM", 
  . "CCK81_LARGE_INTESTINE", "CHL1_SKIN", "CHP212_AUTONOMIC_GANGLIA", 
  . "CI1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "CMK86_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "COLO201_LARGE_INTESTINE", "COLO205_LARGE_INTESTINE", "COLO320_LARGE_INTESTINE", 
  . "COLO677_LUNG", "COLO678_LARGE_INTESTINE", "COLO679_SKIN", "COLO699_LUNG", 
  . "COLO741_SKIN", "CORL105_LUNG", "CORL23_LUNG", "COV318_OVARY", 
  . "COV504_OVARY", "DAOY_CENTRAL_NERVOUS_SYSTEM", "DBTRG05MG_CENTRAL_NERVOUS_SYSTEM", 
  . "DEL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "DETROIT562_UPPER_AERODIGESTIVE_TRACT", 
  . "DKMG_CENTRAL_NERVOUS_SYSTEM", "DMS114_LUNG", "DOHH2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "DOV13_OVARY", "DU145_PROSTATE", "DV90_LUNG", "EB1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "EB2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "EBC1_LUNG", "EFE184_ENDOMETRIUM", 
  . "EFM19_BREAST", "EFO21_OVARY", "EM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "EN_ENDOMETRIUM", "ES2_OVARY", "ESS1_ENDOMETRIUM", "F36P_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "FADU_UPPER_AERODIGESTIVE_TRACT", "FU97_STOMACH", "FUOV1_OVARY", 
  . "G361_SKIN", "G401_SOFT_TISSUE", "G402_SOFT_TISSUE", "GAMG_CENTRAL_NERVOUS_SYSTEM", 
  . "GB1_CENTRAL_NERVOUS_SYSTEM", "GCIY_STOMACH", "GCT_SOFT_TISSUE", 
  . "GI1_CENTRAL_NERVOUS_SYSTEM", "GLC82_LUNG", "GMS10_CENTRAL_NERVOUS_SYSTEM", 
  . "GRANTA519_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "H4_CENTRAL_NERVOUS_SYSTEM", 
  . "HARA_LUNG", "HCC1187_BREAST", "HCC1395_BREAST", "HCC15_LUNG", 
  . "HCC1569_BREAST", "HCC1806_BREAST", "HCC1954_BREAST", "HCC2935_LUNG", 
  . "HCC4006_LUNG", "HCC44_LUNG", "HCC56_LARGE_INTESTINE", "HCC70_BREAST", 
  . "HCC78_LUNG", "HCC827_LUNG", "HCT116_LARGE_INTESTINE", "HCT15_LARGE_INTESTINE", 
  . "HDMYZ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HDQP1_BREAST", "HEC151_ENDOMETRIUM", 
  . "HEC1A_ENDOMETRIUM", "HEC1B_ENDOMETRIUM", "HEC251_ENDOMETRIUM", 
  . "HEC265_ENDOMETRIUM", "HEC59_ENDOMETRIUM", "HEC6_ENDOMETRIUM", 
  . "HEL9217_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HEP3B217_LIVER", 
  . "HEPG2_LIVER", "HGC27_STOMACH", "HH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "HLE_LIVER", "HLF_LIVER", "HMC18_BREAST", "HMCB_SKIN", "HOS_BONE", 
  . "HPAC_PANCREAS", "HPAFII_PANCREAS", "HS229T_LUNG", "HS294T_SKIN", 
  . "HS578T_BREAST", "HS683_CENTRAL_NERVOUS_SYSTEM", "HS695T_SKIN", 
  . "HS729_SOFT_TISSUE", "HS739T_BREAST", "HS746T_STOMACH", "HS766T_PANCREAS", 
  . "HS840T_UPPER_AERODIGESTIVE_TRACT", "HS852T_SKIN", "HS895T_SKIN", 
  . "HS936T_SKIN", "HS939T_SKIN", "HS944T_SKIN", "HSC2_UPPER_AERODIGESTIVE_TRACT", 
  . "HT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HT1080_SOFT_TISSUE", 
  . "HT1197_URINARY_TRACT", "HT1376_URINARY_TRACT", "HT144_SKIN", 
  . "HT29_LARGE_INTESTINE", "HUCCT1_BILIARY_TRACT", "HUH1_LIVER", 
  . "HUPT3_PANCREAS", "HUPT4_PANCREAS", "HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "IALM_LUNG", "IGR37_SKIN", "IGR39_SKIN", "IM95_STOMACH", "IMR32_AUTONOMIC_GANGLIA", 
  . "IPC298_SKIN", "ISHIKAWAHERAKLIO02ER_ENDOMETRIUM", "ISTMES1_PLEURA", 
  . "ISTMES2_PLEURA", "J82_URINARY_TRACT", "JHH2_LIVER", "JHH4_LIVER", 
  . "JHH5_LIVER", "JHH6_LIVER", "JHH7_LIVER", "JHOS2_OVARY", "JHOS4_OVARY", 
  . "JHUEM2_ENDOMETRIUM", "JM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "JMSU1_URINARY_TRACT", "JURKAT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "JVM3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "K029AX_SKIN", "KALS1_CENTRAL_NERVOUS_SYSTEM", 
  . "KARPAS299_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KARPAS422_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KARPAS620_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KASUMI2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KCIMOH1_PANCREAS", "KCL22_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KE39_STOMACH", "KE97_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KELLY_AUTONOMIC_GANGLIA", 
  . "KG1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KHM1B_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KLE_ENDOMETRIUM", "KMBC2_URINARY_TRACT", "KMM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KMRC1_KIDNEY", "KMRC2_KIDNEY", "KMS11_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KMS12BM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS26_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KMS34_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KNS42_CENTRAL_NERVOUS_SYSTEM", 
  . "KNS60_CENTRAL_NERVOUS_SYSTEM", "KNS62_LUNG", "KNS81_CENTRAL_NERVOUS_SYSTEM", 
  . "KO52_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KP1N_PANCREAS", "KP1NL_PANCREAS", 
  . "KP3_PANCREAS", "KP4_PANCREAS", "KPNSI9S_AUTONOMIC_GANGLIA", 
  . "KU812_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KURAMOCHI_OVARY", 
  . "KYM1_SOFT_TISSUE", "KYSE140_OESOPHAGUS", "KYSE150_OESOPHAGUS", 
  . "KYSE180_OESOPHAGUS", "KYSE30_OESOPHAGUS", "KYSE410_OESOPHAGUS", 
  . "KYSE450_OESOPHAGUS", "KYSE510_OESOPHAGUS", "KYSE520_OESOPHAGUS", 
  . "KYSE70_OESOPHAGUS", "L33_PANCREAS", "L363_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "L428_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "LC1SQSF_LUNG", "LCLC103H_LUNG", 
  . "LN229_CENTRAL_NERVOUS_SYSTEM", "LOUNH91_LUNG", "LOXIMVI_SKIN", 
  . "LP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "LS123_LARGE_INTESTINE", 
  . "LS411N_LARGE_INTESTINE", "LS513_LARGE_INTESTINE", "LU99_LUNG", 
  . "LUDLU1_LUNG", "M059J", "MALME3M_SKIN", "MB157_BREAST", "MC116_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "MCAS_OVARY", "MCF7_BREAST", "MDAMB157_BREAST", "MDAMB175VII_BREAST", 
  . "MDAMB415_BREAST", "MDAMB435S_SKIN", "MDAMB436_BREAST", "MDAMB453_BREAST", 
  . "MDAMB468_BREAST", "MEC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "MEG01_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MELHO_SKIN", "MESSA_SOFT_TISSUE", 
  . "MEWO_SKIN", "MFE280_ENDOMETRIUM", "MFE296_ENDOMETRIUM", "MFE319_ENDOMETRIUM", 
  . "MG63_BONE", "MHHES1_BONE", "MIAPACA2_PANCREAS", "MINO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "MJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MKN45_STOMACH", "MKN7_STOMACH", 
  . "MKN74_STOMACH", "MOGGCCM_CENTRAL_NERVOUS_SYSTEM", "MOLP8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "MOLT16_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MONOMAC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "MORCPR_LUNG", "MPP89_PLEURA", "MSTO211H_PLEURA", "NCIH1048_LUNG", 
  . "NCIH1092_LUNG", "NCIH1155_LUNG", "NCIH1184_LUNG", "NCIH1299_LUNG", 
  . "NCIH1339_LUNG", "NCIH1341_LUNG", "NCIH1355_LUNG", "NCIH1373_LUNG", 
  . "NCIH1563_LUNG", "NCIH1568_LUNG", "NCIH1573_LUNG", "NCIH1581_LUNG", 
  . "NCIH1648_LUNG", "NCIH1650_LUNG", "NCIH1651_LUNG", "NCIH1666_LUNG", 
  . "NCIH1693_LUNG", "NCIH1694_LUNG", "NCIH1703_LUNG", "NCIH1792_LUNG", 
  . "NCIH1793_LUNG", "NCIH1869_LUNG", "NCIH1915_LUNG", "NCIH1944_LUNG", 
  . "NCIH1975_LUNG", "NCIH2009_LUNG", "NCIH2023_LUNG", "NCIH2030_LUNG", 
  . "NCIH2052_PLEURA", "NCIH2085_LUNG", "NCIH2087_LUNG", "NCIH211_LUNG", 
  . "NCIH2122_LUNG", "NCIH2170_LUNG", "NCIH2172_LUNG", "NCIH2228_LUNG", 
  . "NCIH226_LUNG", "NCIH2286_LUNG", "NCIH23_LUNG", "NCIH2444_LUNG", 
  . "NCIH2452_PLEURA", "NCIH28_PLEURA", "NCIH322_LUNG", "NCIH3255_LUNG", 
  . "NCIH358_LUNG", "NCIH441_LUNG", "NCIH460_LUNG", "NCIH520_LUNG", 
  . "NCIH522_LUNG", "NCIH647_LUNG", "NCIH650_LUNG", "NCIH661_LUNG", 
  . "NCIH727_LUNG", "NCIH747_LARGE_INTESTINE", "NCIH810_LUNG", "NCIN87_STOMACH", 
  . "NCO2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "NIHOVCAR3_OVARY", 
  . "NUGC3_STOMACH", "NUGC4_STOMACH", "OC314_OVARY", "OC315_OVARY", 
  . "OC316_OVARY", "OCIAML2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "OCIAML5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCILY10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "OCUM1_STOMACH", "OE21_OESOPHAGUS", "OE33_OESOPHAGUS", "ONCODG1_OVARY", 
  . "ONS76_CENTRAL_NERVOUS_SYSTEM", "OPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "OV90_OVARY", "OVCAR4_OVARY", "OVCAR8_OVARY", "OVMANA_OVARY", 
  . "OVSAHO_OVARY", "P12ICHIKAWA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "P31FUJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "PANC0203_PANCREAS", "PANC0327_PANCREAS", "PANC0403_PANCREAS", 
  . "PANC1005_PANCREAS", "PATU8902_PANCREAS", "PC14_LUNG", "PC3_PROSTATE", 
  . "PFEIFFER_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "PK1_PANCREAS", 
  . "PK45H_PANCREAS", "PK59_PANCREAS", "PL45_PANCREAS", "PLCPRF5_LIVER", 
  . "PSN1_PANCREAS", "QGP1_PANCREAS", "RAJI_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "RD_SOFT_TISSUE", "REH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "RERFGC1B_STOMACH", 
  . "RERFLCAI_LUNG", "RERFLCMS_LUNG", "RKN_OVARY", "RKO_LARGE_INTESTINE", 
  . "RL952_ENDOMETRIUM", "RPMI7951_SKIN", "RPMI8402_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "RT112_URINARY_TRACT", "RT4_URINARY_TRACT", "RVH421_SKIN", "SAOS2_BONE", 
  . "SBC5_LUNG", "SCABER_URINARY_TRACT", "SCC25_UPPER_AERODIGESTIVE_TRACT", 
  . "SCC9_UPPER_AERODIGESTIVE_TRACT", "SF126_CENTRAL_NERVOUS_SYSTEM", 
  . "SF295_CENTRAL_NERVOUS_SYSTEM", "SF8657", "SH10TC_STOMACH", "SHP77_LUNG", 
  . "SIGM5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SIMA_AUTONOMIC_GANGLIA", 
  . "SJRH30_SOFT_TISSUE", "SJSA1_BONE", "SKBR3_BREAST", "SKCO1_LARGE_INTESTINE", 
  . "SKES1_BONE", "SKHEP1_LIVER", "SKLMS1_SOFT_TISSUE", "SKLU1_LUNG", 
  . "SKMEL2_SKIN", "SKMEL24_SKIN", "SKMEL30_SKIN", "SKMEL31_SKIN", 
  . "SKMEL5_SKIN", "SKMES1_LUNG", "SKMM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "SKNAS_AUTONOMIC_GANGLIA", "SKNBE2_AUTONOMIC_GANGLIA", "SKNDZ_AUTONOMIC_GANGLIA", 
  . "SKNFI_AUTONOMIC_GANGLIA", "SKNSH_AUTONOMIC_GANGLIA", "SKOV3_OVARY", 
  . "SNGM_ENDOMETRIUM", "SNU1_STOMACH", "SNU16_STOMACH", "SNU182_LIVER", 
  . "SNU387_LIVER", "SNU398_LIVER", "SNU423_LIVER", "SNU449_LIVER", 
  . "SNU475_LIVER", "SNUC2A_LARGE_INTESTINE", "SNUC2B", "SQ1_LUNG", 
  . "SU8686_PANCREAS", "SUDHL10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "SUDHL4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUDHL6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "SUDHL8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUIT2_PANCREAS", 
  . "SUPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUPT1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "SW1088_CENTRAL_NERVOUS_SYSTEM", "SW1271_LUNG", "SW1353_BONE", 
  . "SW1417_LARGE_INTESTINE", "SW1573_LUNG", "SW1990_PANCREAS", "SW403_LARGE_INTESTINE", 
  . "SW48_LARGE_INTESTINE", "SW480_LARGE_INTESTINE", "SW579_THYROID", 
  . "SW620_LARGE_INTESTINE", "SW780_URINARY_TRACT", "SW900_LUNG", 
  . "T24_URINARY_TRACT", "T3M10_LUNG", "T47D_BREAST", "T84_LARGE_INTESTINE", 
  . "T98G_CENTRAL_NERVOUS_SYSTEM", "TC71_BONE", "TCCSUP_URINARY_TRACT", 
  . "TE1_OESOPHAGUS", "TE15_OESOPHAGUS", "TE5_OESOPHAGUS", "TE617T_SOFT_TISSUE", 
  . "TE9_OESOPHAGUS", "TEN_ENDOMETRIUM", "TOLEDO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "TOV112D_OVARY", "TOV21G_OVARY", "TT2609C02_THYROID", "TYKNU_OVARY", 
  . "U118MG_CENTRAL_NERVOUS_SYSTEM", "U2OS_BONE", "U87MG_CENTRAL_NERVOUS_SYSTEM", 
  . "U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "UACC257_SKIN", "UACC62_SKIN", 
  . "UACC812_BREAST", "UMUC3_URINARY_TRACT", "VMRCLCD_LUNG", "VMRCRCW_KIDNEY", 
  . "VMRCRCZ_KIDNEY", "WM115_SKIN", "WM1799_SKIN", "WM2664_SKIN", 
  . "WM793_SKIN", "WM88_SKIN", "WM983B_SKIN", "YKG1_CENTRAL_NERVOUS_SYSTEM", 
  . "ZR751_BREAST", "ZR7530_BREAST", "1321N1_CENTRAL_NERVOUS_SYSTEM", 
  . "22RV1_PROSTATE", "42MGBA_CENTRAL_NERVOUS_SYSTEM", "5637_URINARY_TRACT", 
  . "639V_URINARY_TRACT", "697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "769P_KIDNEY", "786O_KIDNEY", "8305C_THYROID", "8505C_THYROID", 
  . "8MGBA_CENTRAL_NERVOUS_SYSTEM", "A172_CENTRAL_NERVOUS_SYSTEM", 
  . "A204_SOFT_TISSUE", "A2058_SKIN", "A253_SALIVARY_GLAND", "A2780_OVARY", 
  . "A375_SKIN", "A549_LUNG", "A673_BONE", "ACHN_KIDNEY", "ALLSIL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "AMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "AN3CA_ENDOMETRIUM", 
  . "ASPC1_PANCREAS", "AU565_BREAST", "AZ521_STOMACH", "BCPAP_THYROID", 
  . "BDCM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BFTC909_KIDNEY", 
  . "BGC823_STOMACH", "BL41_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "BL70_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BT20_BREAST", "BT474_BREAST", 
  . "BT549_BREAST", "BXPC3_PANCREAS", "C2BBE1_LARGE_INTESTINE", "C32_SKIN", 
  . "C3A_LIVER", "CAKI2_KIDNEY", "CAL12T_LUNG", "CAL27_UPPER_AERODIGESTIVE_TRACT", 
  . "CAL78_BONE", "CAL851_BREAST", "CALU1_LUNG", "CALU3_LUNG", "CALU6_LUNG", 
  . "CAMA1_BREAST", "CAPAN2_PANCREAS", "CAS1_CENTRAL_NERVOUS_SYSTEM", 
  . "CCK81_LARGE_INTESTINE", "CHL1_SKIN", "CHP212_AUTONOMIC_GANGLIA", 
  . "CI1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "CMK86_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "COLO201_LARGE_INTESTINE", "COLO205_LARGE_INTESTINE", "COLO320_LARGE_INTESTINE", 
  . "COLO677_LUNG", "COLO678_LARGE_INTESTINE", "COLO679_SKIN", "COLO699_LUNG", 
  . "COLO741_SKIN", "CORL105_LUNG", "CORL23_LUNG", "COV318_OVARY", 
  . "COV504_OVARY", "DAOY_CENTRAL_NERVOUS_SYSTEM", "DBTRG05MG_CENTRAL_NERVOUS_SYSTEM", 
  . "DEL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "DETROIT562_UPPER_AERODIGESTIVE_TRACT", 
  . "DKMG_CENTRAL_NERVOUS_SYSTEM", "DMS114_LUNG", "DOHH2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "DOV13_OVARY", "DU145_PROSTATE", "DV90_LUNG", "EB1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "EB2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "EBC1_LUNG", "EFE184_ENDOMETRIUM", 
  . "EFM19_BREAST", "EFO21_OVARY", "EFO27_OVARY", "EM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "EN_ENDOMETRIUM", "ES2_OVARY", "ESS1_ENDOMETRIUM", "F36P_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "FADU_UPPER_AERODIGESTIVE_TRACT", "FU97_STOMACH", "FUOV1_OVARY", 
  . "G361_SKIN", "G401_SOFT_TISSUE", "G402_SOFT_TISSUE", "GAMG_CENTRAL_NERVOUS_SYSTEM", 
  . "GB1_CENTRAL_NERVOUS_SYSTEM", "GCIY_STOMACH", "GCT_SOFT_TISSUE", 
  . "GI1_CENTRAL_NERVOUS_SYSTEM", "GLC82_LUNG", "GMS10_CENTRAL_NERVOUS_SYSTEM", 
  . "GRANTA519_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "H4_CENTRAL_NERVOUS_SYSTEM", 
  . "HARA_LUNG", "HCC1187_BREAST", "HCC1395_BREAST", "HCC15_LUNG", 
  . "HCC1569_BREAST", "HCC1806_BREAST", "HCC1954_BREAST", "HCC2935_LUNG", 
  . "HCC4006_LUNG", "HCC44_LUNG", "HCC56_LARGE_INTESTINE", "HCC70_BREAST", 
  . "HCC78_LUNG", "HCC827_LUNG", "HCT116_LARGE_INTESTINE", "HCT15_LARGE_INTESTINE", 
  . "HDMYZ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HDQP1_BREAST", "HEC151_ENDOMETRIUM", 
  . "HEC1A_ENDOMETRIUM", "HEC1B_ENDOMETRIUM", "HEC251_ENDOMETRIUM", 
  . "HEC265_ENDOMETRIUM", "HEC59_ENDOMETRIUM", "HEC6_ENDOMETRIUM", 
  . "HEL9217_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HEP3B217_LIVER", 
  . "HEPG2_LIVER", "HEYA8_OVARY", "HGC27_STOMACH", "HH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "HLE_LIVER", "HLF_LIVER", "HMC18_BREAST", "HMCB_SKIN", "HOS_BONE", 
  . "HPAC_PANCREAS", "HPAFII_PANCREAS", "HS229T_LUNG", "HS294T_SKIN", 
  . "HS578T_BREAST", "HS683_CENTRAL_NERVOUS_SYSTEM", "HS695T_SKIN", 
  . "HS729_SOFT_TISSUE", "HS739T_BREAST", "HS746T_STOMACH", "HS766T_PANCREAS", 
  . "HS840T_UPPER_AERODIGESTIVE_TRACT", "HS852T_SKIN", "HS895T_SKIN", 
  . "HS936T_SKIN", "HS939T_SKIN", "HS944T_SKIN", "HSC2_UPPER_AERODIGESTIVE_TRACT", 
  . "HT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HT1080_SOFT_TISSUE", 
  . "HT1197_URINARY_TRACT", "HT1376_URINARY_TRACT", "HT144_SKIN", 
  . "HT29_LARGE_INTESTINE", "HUCCT1_BILIARY_TRACT", "HUH1_LIVER", 
  . "HUPT3_PANCREAS", "HUPT4_PANCREAS", "HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "IALM_LUNG", "IGR37_SKIN", "IGR39_SKIN", "IGROV1_OVARY", "IM95_STOMACH", 
  . "IMR32_AUTONOMIC_GANGLIA", "IPC298_SKIN", "ISHIKAWAHERAKLIO02ER_ENDOMETRIUM", 
  . "ISTMES1_PLEURA", "ISTMES2_PLEURA", "J82_URINARY_TRACT", "JHH2_LIVER", 
  . "JHH4_LIVER", "JHH5_LIVER", "JHH6_LIVER", "JHH7_LIVER", "JHOS2_OVARY", 
  . "JHOS4_OVARY", "JHUEM2_ENDOMETRIUM", "JM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "JMSU1_URINARY_TRACT", "JURKAT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "JVM3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "K029AX_SKIN", "KALS1_CENTRAL_NERVOUS_SYSTEM", 
  . "KARPAS299_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KARPAS422_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KARPAS620_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KASUMI2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KCIMOH1_PANCREAS", "KCL22_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KE39_STOMACH", "KE97_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KELLY_AUTONOMIC_GANGLIA", 
  . "KG1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KHM1B_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KLE_ENDOMETRIUM", "KMBC2_URINARY_TRACT", "KMM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KMRC1_KIDNEY", "KMRC2_KIDNEY", "KMS11_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KMS12BM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS26_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "KMS34_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KNS42_CENTRAL_NERVOUS_SYSTEM", 
  . "KNS60_CENTRAL_NERVOUS_SYSTEM", "KNS62_LUNG", "KNS81_CENTRAL_NERVOUS_SYSTEM", 
  . "KO52_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KP1N_PANCREAS", "KP1NL_PANCREAS", 
  . "KP2_PANCREAS", "KP3_PANCREAS", "KP4_PANCREAS", "KPNSI9S_AUTONOMIC_GANGLIA", 
  . "KU812_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KURAMOCHI_OVARY", 
  . "KYM1_SOFT_TISSUE", "KYSE140_OESOPHAGUS", "KYSE150_OESOPHAGUS", 
  . "KYSE180_OESOPHAGUS", "KYSE30_OESOPHAGUS", "KYSE410_OESOPHAGUS", 
  . "KYSE450_OESOPHAGUS", "KYSE510_OESOPHAGUS", "KYSE520_OESOPHAGUS", 
  . "KYSE70_OESOPHAGUS", "L33_PANCREAS", "L363_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "L428_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "LC1SQSF_LUNG", "LCLC103H_LUNG", 
  . "LN18_CENTRAL_NERVOUS_SYSTEM", "LN229_CENTRAL_NERVOUS_SYSTEM", 
  . "LOUNH91_LUNG", "LOXIMVI_SKIN", "LP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "LS123_LARGE_INTESTINE", "LS411N_LARGE_INTESTINE", "LS513_LARGE_INTESTINE", 
  . "LU99_LUNG", "LUDLU1_LUNG", "M059J", "MALME3M_SKIN", "MB157_BREAST", 
  . "MC116_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MCAS_OVARY", "MCF7_BREAST", 
  . "MDAMB157_BREAST", "MDAMB175VII_BREAST", "MDAMB415_BREAST", "MDAMB435S_SKIN", 
  . "MDAMB436_BREAST", "MDAMB453_BREAST", "MDAMB468_BREAST", "MEC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "MEG01_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MELHO_SKIN", "MESSA_SOFT_TISSUE", 
  . "MEWO_SKIN", "MFE280_ENDOMETRIUM", "MFE296_ENDOMETRIUM", "MFE319_ENDOMETRIUM", 
  . "MG63_BONE", "MHHES1_BONE", "MIAPACA2_PANCREAS", "MINO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "MJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MKN45_STOMACH", "MKN7_STOMACH", 
  . "MKN74_STOMACH", "MOGGCCM_CENTRAL_NERVOUS_SYSTEM", "MOLP8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "MOLT16_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MONOMAC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "MORCPR_LUNG", "MPP89_PLEURA", "MSTO211H_PLEURA", "NCIH1048_LUNG", 
  . "NCIH1092_LUNG", "NCIH1155_LUNG", "NCIH1184_LUNG", "NCIH1299_LUNG", 
  . "NCIH1339_LUNG", "NCIH1341_LUNG", "NCIH1355_LUNG", "NCIH1373_LUNG", 
  . "NCIH1563_LUNG", "NCIH1568_LUNG", "NCIH1573_LUNG", "NCIH1581_LUNG", 
  . "NCIH1648_LUNG", "NCIH1650_LUNG", "NCIH1651_LUNG", "NCIH1666_LUNG", 
  . "NCIH1693_LUNG", "NCIH1694_LUNG", "NCIH1703_LUNG", "NCIH1792_LUNG", 
  . "NCIH1793_LUNG", "NCIH1869_LUNG", "NCIH1915_LUNG", "NCIH1944_LUNG", 
  . "NCIH1975_LUNG", "NCIH2009_LUNG", "NCIH2023_LUNG", "NCIH2030_LUNG", 
  . "NCIH2052_PLEURA", "NCIH2085_LUNG", "NCIH2087_LUNG", "NCIH211_LUNG", 
  . "NCIH2122_LUNG", "NCIH2170_LUNG", "NCIH2172_LUNG", "NCIH2228_LUNG", 
  . "NCIH226_LUNG", "NCIH2286_LUNG", "NCIH23_LUNG", "NCIH2444_LUNG", 
  . "NCIH2452_PLEURA", "NCIH28_PLEURA", "NCIH322_LUNG", "NCIH3255_LUNG", 
  . "NCIH358_LUNG", "NCIH441_LUNG", "NCIH460_LUNG", "NCIH520_LUNG", 
  . "NCIH522_LUNG", "NCIH647_LUNG", "NCIH650_LUNG", "NCIH661_LUNG", 
  . "NCIH727_LUNG", "NCIH747_LARGE_INTESTINE", "NCIH810_LUNG", "NCIN87_STOMACH", 
  . "NCO2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "NIHOVCAR3_OVARY", 
  . "NUGC3_STOMACH", "NUGC4_STOMACH", "OC314_OVARY", "OC315_OVARY", 
  . "OC316_OVARY", "OCIAML2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "OCIAML5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCILY10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "OCUM1_STOMACH", "OE21_OESOPHAGUS", "OE33_OESOPHAGUS", "ONCODG1_OVARY", 
  . "ONS76_CENTRAL_NERVOUS_SYSTEM", "OPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "OV90_OVARY", "OVCAR4_OVARY", "OVCAR8_OVARY", "OVMANA_OVARY", 
  . "OVSAHO_OVARY", "OVTOKO_OVARY", "P12ICHIKAWA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "P31FUJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "PANC0203_PANCREAS", "PANC0327_PANCREAS", "PANC0403_PANCREAS", 
  . "PANC1005_PANCREAS", "PATU8902_PANCREAS", "PC14_LUNG", "PC3_PROSTATE", 
  . "PFEIFFER_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "PK1_PANCREAS", 
  . "PK45H_PANCREAS", "PK59_PANCREAS", "PL45_PANCREAS", "PLCPRF5_LIVER", 
  . "PSN1_PANCREAS", "QGP1_PANCREAS", "RAJI_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "RD_SOFT_TISSUE", "REH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "RERFGC1B_STOMACH", 
  . "RERFLCAI_LUNG", "RERFLCMS_LUNG", "RKN_OVARY", "RKO_LARGE_INTESTINE", 
  . "RL952_ENDOMETRIUM", "RPMI7951_SKIN", "RPMI8402_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "RT112_URINARY_TRACT", "RT4_URINARY_TRACT", "RVH421_SKIN", "SAOS2_BONE", 
  . "SBC5_LUNG", "SCABER_URINARY_TRACT", "SCC9_UPPER_AERODIGESTIVE_TRACT", 
  . "SF126_CENTRAL_NERVOUS_SYSTEM", "SF295_CENTRAL_NERVOUS_SYSTEM", 
  . "SF8657", "SH10TC_STOMACH", "SHP77_LUNG", "SIGM5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "SIMA_AUTONOMIC_GANGLIA", "SJRH30_SOFT_TISSUE", "SJSA1_BONE", 
  . "SKBR3_BREAST", "SKCO1_LARGE_INTESTINE", "SKES1_BONE", "SKHEP1_LIVER", 
  . "SKLMS1_SOFT_TISSUE", "SKLU1_LUNG", "SKMEL2_SKIN", "SKMEL24_SKIN", 
  . "SKMEL30_SKIN", "SKMEL31_SKIN", "SKMEL5_SKIN", "SKMES1_LUNG", 
  . "SKMM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SKNAS_AUTONOMIC_GANGLIA", 
  . "SKNBE2_AUTONOMIC_GANGLIA", "SKNDZ_AUTONOMIC_GANGLIA", "SKNFI_AUTONOMIC_GANGLIA", 
  . "SKNSH_AUTONOMIC_GANGLIA", "SKOV3_OVARY", "SNGM_ENDOMETRIUM", 
  . "SNU1_STOMACH", "SNU16_STOMACH", "SNU182_LIVER", "SNU387_LIVER", 
  . "SNU398_LIVER", "SNU423_LIVER", "SNU449_LIVER", "SNU475_LIVER", 
  . "SNUC2A_LARGE_INTESTINE", "SNUC2B", "SQ1_LUNG", "SU8686_PANCREAS", 
  . "SUDHL10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUDHL4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "SUDHL6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUDHL8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "SUIT2_PANCREAS", "SUPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "SUPT1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SW1088_CENTRAL_NERVOUS_SYSTEM", 
  . "SW1271_LUNG", "SW1353_BONE", "SW1417_LARGE_INTESTINE", "SW1573_LUNG", 
  . "SW1990_PANCREAS", "SW403_LARGE_INTESTINE", "SW48_LARGE_INTESTINE", 
  . "SW480_LARGE_INTESTINE", "SW579_THYROID", "SW620_LARGE_INTESTINE", 
  . "SW780_URINARY_TRACT", "SW900_LUNG", "T24_URINARY_TRACT", "T3M10_LUNG", 
  . "T47D_BREAST", "T84_LARGE_INTESTINE", "T98G_CENTRAL_NERVOUS_SYSTEM", 
  . "TC71_BONE", "TCCSUP_URINARY_TRACT", "TE1_OESOPHAGUS", "TE11_OESOPHAGUS", 
  . "TE15_OESOPHAGUS", "TE5_OESOPHAGUS", "TE617T_SOFT_TISSUE", "TE9_OESOPHAGUS", 
  . "TEN_ENDOMETRIUM", "TOLEDO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  . "TOV112D_OVARY", "TOV21G_OVARY", "TT2609C02_THYROID", "TYKNU_OVARY", 
  . "U118MG_CENTRAL_NERVOUS_SYSTEM", "U2OS_BONE", "U87MG_CENTRAL_NERVOUS_SYSTEM", 
  . "U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "UACC257_SKIN", "UACC62_SKIN", 
  . "UACC812_BREAST", "UMUC3_URINARY_TRACT", "VMRCLCD_LUNG", "VMRCRCW_KIDNEY", 
  . "VMRCRCZ_KIDNEY", "WM115_SKIN", "WM1799_SKIN", "WM2664_SKIN", 
  . "WM793_SKIN", "WM88_SKIN", "WM983B_SKIN", "YKG1_CENTRAL_NERVOUS_SYSTEM", 
  . "ZR751_BREAST", "ZR7530_BREAST"), subscript_type = "character", 
  .     names = c("Name", "Description", "22RV1_PROSTATE", "2313287_STOMACH", 
  .     "253JBV_URINARY_TRACT", "253J_URINARY_TRACT", "42MGBA_CENTRAL_NERVOUS_SYSTEM", 
  .     "5637_URINARY_TRACT", "59M_OVARY", "639V_URINARY_TRACT", 
  .     "647V_URINARY_TRACT", "697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "769P_KIDNEY", "786O_KIDNEY", "8305C_THYROID", "8505C_THYROID", 
  .     "8MGBA_CENTRAL_NERVOUS_SYSTEM", "A101D_SKIN", "A1207_CENTRAL_NERVOUS_SYSTEM", 
  .     "A172_CENTRAL_NERVOUS_SYSTEM", "A204_SOFT_TISSUE", "A2058_SKIN", 
  .     "A253_SALIVARY_GLAND", "A2780_OVARY", "A375_SKIN", "A3KAW_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "A427_LUNG", "A498_KIDNEY", "A4FUK_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "A549_LUNG", "A673_BONE", "A704_KIDNEY", "ABC1_LUNG", "ACCMESO1_PLEURA", 
  .     "ACHN_KIDNEY", "AGS_STOMACH", "ALLSIL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "AM38_CENTRAL_NERVOUS_SYSTEM", "AML193_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "AMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "AN3CA_ENDOMETRIUM", 
  .     "ASPC1_PANCREAS", "AU565_BREAST", "BC3C_URINARY_TRACT", "BCP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "BCPAP_THYROID", "BDCM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "BEN_LUNG", "BFTC905_URINARY_TRACT", "BFTC909_KIDNEY", "BHT101_THYROID", 
  .     "BHY_UPPER_AERODIGESTIVE_TRACT", "BICR16_UPPER_AERODIGESTIVE_TRACT", 
  .     "BICR18_UPPER_AERODIGESTIVE_TRACT", "BICR22_UPPER_AERODIGESTIVE_TRACT", 
  .     "BICR31_UPPER_AERODIGESTIVE_TRACT", "BICR56_UPPER_AERODIGESTIVE_TRACT", 
  .     "BICR6_UPPER_AERODIGESTIVE_TRACT", "BL41_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "BL70_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BT12_SOFT_TISSUE", 
  .     "BT20_BREAST", "BT474_BREAST", "BT483_BREAST", "BT549_BREAST", 
  .     "BV173_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BXPC3_PANCREAS", 
  .     "C2BBE1_LARGE_INTESTINE", "C32_SKIN", "C8166_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "CA46_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "CADOES1_BONE", 
  .     "CAKI1_KIDNEY", "CAKI2_KIDNEY", "CAL120_BREAST", "CAL12T_LUNG", 
  .     "CAL148_BREAST", "CAL27_UPPER_AERODIGESTIVE_TRACT", "CAL29_URINARY_TRACT", 
  .     "CAL33_UPPER_AERODIGESTIVE_TRACT", "CAL51_BREAST", "CAL54_KIDNEY", 
  .     "CAL62_THYROID", "CAL78_BONE", "CAL851_BREAST", "CALU1_LUNG", 
  .     "CALU3_LUNG", "CALU6_LUNG", "CAMA1_BREAST", "CAOV3_OVARY", 
  .     "CAOV4_OVARY", "CAPAN1_PANCREAS", "CAPAN2_PANCREAS", "CAS1_CENTRAL_NERVOUS_SYSTEM", 
  .     "CCFSTTG1_CENTRAL_NERVOUS_SYSTEM", "CCK81_LARGE_INTESTINE", 
  .     "CFPAC1_PANCREAS", "CH157MN_CENTRAL_NERVOUS_SYSTEM", "CHAGOK1_LUNG", 
  .     "CHP126_AUTONOMIC_GANGLIA", "CHP212_AUTONOMIC_GANGLIA", "CI1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "CJM_SKIN", "CL11_LARGE_INTESTINE", "CL14_LARGE_INTESTINE", 
  .     "CL34_LARGE_INTESTINE", "CL40_LARGE_INTESTINE", "CMK_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "CMLT1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "COLO201_LARGE_INTESTINE", 
  .     "COLO320_LARGE_INTESTINE", "COLO668_LUNG", "COLO678_LARGE_INTESTINE", 
  .     "COLO679_SKIN", "COLO680N_OESOPHAGUS", "COLO684_ENDOMETRIUM", 
  .     "COLO741_SKIN", "COLO783_SKIN", "COLO792_SKIN", "COLO800_SKIN", 
  .     "COLO829_SKIN", "CORL105_LUNG", "CORL23_LUNG", "CORL24_LUNG", 
  .     "CORL279_LUNG", "CORL311_LUNG", "CORL47_LUNG", "CORL88_LUNG", 
  .     "CORL95_LUNG", "COV318_OVARY", "COV362_OVARY", "COV434_OVARY", 
  .     "COV644_OVARY", "CW2_LARGE_INTESTINE", "CACO2_LARGE_INTESTINE", 
  .     "D283MED_CENTRAL_NERVOUS_SYSTEM", "D341MED_CENTRAL_NERVOUS_SYSTEM", 
  .     "DANG_PANCREAS", "DAOY_CENTRAL_NERVOUS_SYSTEM", "DAUDI_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "DBTRG05MG_CENTRAL_NERVOUS_SYSTEM", "DB_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "DEL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "DETROIT562_UPPER_AERODIGESTIVE_TRACT", 
  .     "DKMG_CENTRAL_NERVOUS_SYSTEM", "DM3_FIBROBLAST", "DMS114_LUNG", 
  .     "DMS153_LUNG", "DMS273_LUNG", "DMS454_LUNG", "DMS53_LUNG", 
  .     "DMS79_LUNG", "DND41_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "DOHH2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "DU145_PROSTATE", 
  .     "DU4475_BREAST", "DV90_LUNG", "EB1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "EBC1_LUNG", "ECC10_STOMACH", "ECC12_STOMACH", "ECGI10_OESOPHAGUS", 
  .     "EFE184_ENDOMETRIUM", "EFM192A_BREAST", "EFM19_BREAST", "EFO21_OVARY", 
  .     "EFO27_OVARY", "EHEB_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "EJM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "EKVX_LUNG", "EM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "EN_ENDOMETRIUM", "EOL1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "EPLC272H_LUNG", "ES2_OVARY", "ESS1_ENDOMETRIUM", "EW8_BONE", 
  .     "EWS502_BONE", "F36P_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "FADU_UPPER_AERODIGESTIVE_TRACT", "FTC133_THYROID", "FTC238_THYROID", 
  .     "FU97_STOMACH", "FUOV1_OVARY", "G292CLONEA141B1_BONE", "G361_SKIN", 
  .     "G401_SOFT_TISSUE", "G402_SOFT_TISSUE", "GA10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "GAMG_CENTRAL_NERVOUS_SYSTEM", "GB1_CENTRAL_NERVOUS_SYSTEM", 
  .     "GCIY_STOMACH", "GCT_SOFT_TISSUE", "GDM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "GI1_CENTRAL_NERVOUS_SYSTEM", "GMS10_CENTRAL_NERVOUS_SYSTEM", 
  .     "GOS3_CENTRAL_NERVOUS_SYSTEM", "GP2D_LARGE_INTESTINE", "GRANTA519_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "GSS_STOMACH", "GSU_STOMACH", "H4_CENTRAL_NERVOUS_SYSTEM", 
  .     "HARA_LUNG", "HCC1143_BREAST", "HCC1171_LUNG", "HCC1187_BREAST", 
  .     "HCC1195_LUNG", "HCC1359_LUNG", "HCC1395_BREAST", "HCC1419_BREAST", 
  .     "HCC1428_BREAST", "HCC1438_LUNG", "HCC1500_BREAST", "HCC1569_BREAST", 
  .     "HCC1588_LUNG", "HCC1599_BREAST", "HCC15_LUNG", "HCC1806_BREAST", 
  .     "HCC1833_LUNG", "HCC1937_BREAST", "HCC1954_BREAST", "HCC202_BREAST", 
  .     "HCC2108_LUNG", "HCC2157_BREAST", "HCC2218_BREAST", "HCC2279_LUNG", 
  .     "HCC2429_LUNG", "HCC2450_LUNG", "HCC2814_LUNG", "HCC2935_LUNG", 
  .     "HCC33_LUNG", "HCC364_LUNG", "HCC366_LUNG", "HCC38_BREAST", 
  .     "HCC4006_LUNG", "HCC44_LUNG", "HCC461_LUNG", "HCC515_LUNG", 
  .     "HCC56_LARGE_INTESTINE", "HCC70_BREAST", "HCC78_LUNG", "HCC827GR5_LUNG", 
  .     "HCC827_LUNG", "HCC95_LUNG", "HCT116_LARGE_INTESTINE", "HCT15_LARGE_INTESTINE", 
  .     "HDLM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HDMYZ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "HDQP1_BREAST", "HEC108_ENDOMETRIUM", "HEC151_ENDOMETRIUM", 
  .     "HEC1A_ENDOMETRIUM", "HEC1B_ENDOMETRIUM", "HEC251_ENDOMETRIUM", 
  .     "HEC265_ENDOMETRIUM", "HEC50B_ENDOMETRIUM", "HEC59_ENDOMETRIUM", 
  .     "HEC6_ENDOMETRIUM", "HEKTE_KIDNEY", "HEL9217_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "HEL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HEP3B217_LIVER", 
  .     "HEPG2_LIVER", "HEYA8_OVARY", "HGC27_STOMACH", "HH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "HL60_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HLFA_FIBROBLAST", 
  .     "HLF_LIVER", "HMC18_BREAST", "HMCB_SKIN", "HMEL_BREAST", 
  .     "HNT34_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HOP62_LUNG", 
  .     "HOP92_LUNG", "HOS_BONE", "HPAC_PANCREAS", "HPAFII_PANCREAS", 
  .     "HPBALL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HS172T_FIBROBLAST", 
  .     "HS229T_FIBROBLAST", "HS255T_FIBROBLAST", "HS274T_BREAST", 
  .     "HS281T_FIBROBLAST", "HS294T_SKIN", "HS343T_FIBROBLAST", 
  .     "HS578T_BREAST", "HS600T_FIBROBLAST", "HS606T_FIBROBLAST", 
  .     "HS611T_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HS616T_FIBROBLAST", 
  .     "HS618T_FIBROBLAST", "HS675T_FIBROBLAST", "HS683_CENTRAL_NERVOUS_SYSTEM", 
  .     "HS688AT_FIBROBLAST", "HS695T_SKIN", "HS698T_FIBROBLAST", 
  .     "HS706T_BONE", "HS729_SOFT_TISSUE", "HS737T_FIBROBLAST", 
  .     "HS739T_FIBROBLAST", "HS742T_FIBROBLAST", "HS746T_STOMACH", 
  .     "HS751T_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HS766T_PANCREAS", 
  .     "HS819T_FIBROBLAST", "HS821T_FIBROBLAST", "HS822T_FIBROBLAST", 
  .     "HS834T_FIBROBLAST", "HS839T_FIBROBLAST", "HS840T_FIBROBLAST", 
  .     "HS852T_SKIN", "HS863T_FIBROBLAST", "HS870T_FIBROBLAST", 
  .     "HS888T_FIBROBLAST", "HS895T_FIBROBLAST", "HS934T_FIBROBLAST", 
  .     "HS936T_SKIN", "HS939T_SKIN", "HS940T_FIBROBLAST", "HS944T_SKIN", 
  .     "HSC2_UPPER_AERODIGESTIVE_TRACT", "HSC3_UPPER_AERODIGESTIVE_TRACT", 
  .     "HSC4_UPPER_AERODIGESTIVE_TRACT", "HT1080_SOFT_TISSUE", "HT115_LARGE_INTESTINE", 
  .     "HT1197_URINARY_TRACT", "HT1376_URINARY_TRACT", "HT144_SKIN", 
  .     "HT29_LARGE_INTESTINE", "HT55_LARGE_INTESTINE", "HT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "HUCCT1_BILIARY_TRACT", "HUG1N_STOMACH", "HUH1_LIVER", "HUH28_BILIARY_TRACT", 
  .     "HUH6_LIVER", "HUH7_LIVER", "HUNS1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "HUPT3_PANCREAS", "HUPT4_PANCREAS", "HUT102_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HUTU80_SMALL_INTESTINE", 
  .     "HELA_CERVIX", "IALM_LUNG", "IGR1_SKIN", "IGR37_SKIN", "IGR39_SKIN", 
  .     "IGROV1_OVARY", "IM95_STOMACH", "IMR32_AUTONOMIC_GANGLIA", 
  .     "INA6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "IOMMLEE_CENTRAL_NERVOUS_SYSTEM", 
  .     "IPC298_SKIN", "ISHIKAWAHERAKLIO02ER_ENDOMETRIUM", "ISTMES1_PLEURA", 
  .     "ISTMES2_PLEURA", "J82_URINARY_TRACT", "JEKO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "JHESOAD1_OESOPHAGUS", "JHH1_LIVER", "JHH2_LIVER", "JHH4_LIVER", 
  .     "JHH5_LIVER", "JHH6_LIVER", "JHH7_LIVER", "JHOC5_OVARY", 
  .     "JHOM1_OVARY", "JHOM2B_OVARY", "JHOS2_OVARY", "JHOS4_OVARY", 
  .     "JHUEM1_ENDOMETRIUM", "JHUEM2_ENDOMETRIUM", "JHUEM3_ENDOMETRIUM", 
  .     "JHUEM7_ENDOMETRIUM", "JIMT1_BREAST", "JJN3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "JK1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "JL1_PLEURA", "JM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "JMSU1_URINARY_TRACT", "JURKAT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "JURLMK1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "JVM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "JVM3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "K029AX_SKIN", 
  .     "K562_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KALS1_CENTRAL_NERVOUS_SYSTEM", 
  .     "KARPAS299_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KARPAS422_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KARPAS620_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KASUMI1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KASUMI2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KASUMI6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KATOIII_STOMACH", "KCL22_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KD_SOFT_TISSUE", "KE37_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KE39_STOMACH", "KE97_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KELLY_AUTONOMIC_GANGLIA", "KG1C_CENTRAL_NERVOUS_SYSTEM", 
  .     "KG1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KHM1B_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KIJK_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KLE_ENDOMETRIUM", 
  .     "KM12_LARGE_INTESTINE", "KMBC2_URINARY_TRACT", "KMH2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KMM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMRC1_KIDNEY", 
  .     "KMRC20_KIDNEY", "KMRC2_KIDNEY", "KMRC3_KIDNEY", "KMS18_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KMS11_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS12BM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KMS20_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS21BM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KMS26_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS27_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KMS28BM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS34_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KNS42_CENTRAL_NERVOUS_SYSTEM", "KNS60_CENTRAL_NERVOUS_SYSTEM", 
  .     "KNS62_LUNG", "KNS81_CENTRAL_NERVOUS_SYSTEM", "KO52_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KOPN8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KP2_PANCREAS", 
  .     "KP3_PANCREAS", "KP4_PANCREAS", "KPL1_BREAST", "KPMRTRY_SOFT_TISSUE", 
  .     "KPNRTBM1_AUTONOMIC_GANGLIA", "KPNSI9S_AUTONOMIC_GANGLIA", 
  .     "KPNYN_AUTONOMIC_GANGLIA", "KS1_CENTRAL_NERVOUS_SYSTEM", 
  .     "KU1919_URINARY_TRACT", "KU812_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KURAMOCHI_OVARY", "KYM1_SOFT_TISSUE", "KYO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "KYSE140_OESOPHAGUS", "KYSE150_OESOPHAGUS", "KYSE180_OESOPHAGUS", 
  .     "KYSE270_OESOPHAGUS", "KYSE30_OESOPHAGUS", "KYSE410_OESOPHAGUS", 
  .     "KYSE450_OESOPHAGUS", "KYSE510_OESOPHAGUS", "KYSE520_OESOPHAGUS", 
  .     "KYSE70_OESOPHAGUS", "L1236_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "L33_PANCREAS", "L363_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "L428_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "L540_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "LAMA84_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "LC1F_LUNG", 
  .     "LCLC103H_LUNG", "LCLC97TM1_LUNG", "LI7_LIVER", "LK2_LUNG", 
  .     "LMSU_STOMACH", "LN18_CENTRAL_NERVOUS_SYSTEM", "LN215_CENTRAL_NERVOUS_SYSTEM", 
  .     "LN229_CENTRAL_NERVOUS_SYSTEM", "LN235_CENTRAL_NERVOUS_SYSTEM", 
  .     "LN319_CENTRAL_NERVOUS_SYSTEM", "LN340_CENTRAL_NERVOUS_SYSTEM", 
  .     "LN382_CENTRAL_NERVOUS_SYSTEM", "LN428_CENTRAL_NERVOUS_SYSTEM", 
  .     "LN443_CENTRAL_NERVOUS_SYSTEM", "LN464_CENTRAL_NERVOUS_SYSTEM", 
  .     "LNCAPCLONEFGC_PROSTATE", "LNZ308_CENTRAL_NERVOUS_SYSTEM", 
  .     "LOUCY_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "LOUNH91_LUNG", 
  .     "LOVO_LARGE_INTESTINE", "LOXIMVI_SKIN", "LP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "LS1034_LARGE_INTESTINE", "LS123_LARGE_INTESTINE", "LS180_LARGE_INTESTINE", 
  .     "LS411N_LARGE_INTESTINE", "LS513_LARGE_INTESTINE", "LU65_LUNG", 
  .     "LU99_LUNG", "LUDLU1_LUNG", "LXF289_LUNG", "M059K_CENTRAL_NERVOUS_SYSTEM", 
  .     "M07E_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MALME3M_SKIN", 
  .     "MC116_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MCAS_OVARY", 
  .     "MCF7_BREAST", "MDAMB134VI_BREAST", "MDAMB157_BREAST", "MDAMB175VII_BREAST", 
  .     "MDAMB231_BREAST", "MDAMB361_BREAST", "MDAMB415_BREAST", 
  .     "MDAMB435S_SKIN", "MDAMB436_BREAST", "MDAMB453_BREAST", "MDAMB468_BREAST", 
  .     "MDAPCA2B_PROSTATE", "MDST8_LARGE_INTESTINE", "ME1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "MEC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MEG01_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "MELHO_SKIN", "MELJUSO_SKIN", "MESSA_SOFT_TISSUE", "MEWO_SKIN", 
  .     "MFE280_ENDOMETRIUM", "MFE296_ENDOMETRIUM", "MFE319_ENDOMETRIUM", 
  .     "MG63_BONE", "MHHCALL2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "MHHCALL3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MHHCALL4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "MHHES1_BONE", "MHHNB11_AUTONOMIC_GANGLIA", "MIAPACA2_PANCREAS", 
  .     "MINO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "MKN1_STOMACH", "MKN45_STOMACH", "MKN74_STOMACH", "MKN7_STOMACH", 
  .     "ML1_THYROID", "MM1S_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "MOLM13_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MOLM16_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "MOLM6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MOLP2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "MOLP8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MOLT13_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "MOLT16_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MOLT3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "MON_SOFT_TISSUE", "MONOMAC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "MONOMAC6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MORCPR_LUNG", 
  .     "MPP89_PLEURA", "MSTO211H_PLEURA", "MUTZ3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "MUTZ5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MV411_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "NALM19_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "NALM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "NALM6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "NAMALWA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "NB1_AUTONOMIC_GANGLIA", "NB4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "NCCSTCK140_STOMACH", "NCIH2004RT_SOFT_TISSUE", "NCIH2077_LUNG", 
  .     "NCIH2882_LUNG", "NCIH2887_LUNG", "NCIH1048_LUNG", "NCIH1092_LUNG", 
  .     "NCIH1105_LUNG", "NCIH1155_LUNG", "NCIH1184_LUNG", "NCIH1299_LUNG", 
  .     "NCIH1339_LUNG", "NCIH1341_LUNG", "NCIH1355_LUNG", "NCIH1373_LUNG", 
  .     "NCIH1385_LUNG", "NCIH1395_LUNG", "NCIH1435_LUNG", "NCIH1436_LUNG", 
  .     "NCIH1437_LUNG", "NCIH146_LUNG", "NCIH1563_LUNG", "NCIH1568_LUNG", 
  .     "NCIH1573_LUNG", "NCIH1581_LUNG", "NCIH1618_LUNG", "NCIH1623_LUNG", 
  .     "NCIH1648_LUNG", "NCIH1650_LUNG", "NCIH1651_LUNG", "NCIH1666_LUNG", 
  .     "NCIH1693_LUNG", "NCIH1694_LUNG", "NCIH1703_LUNG", "NCIH1734_LUNG", 
  .     "NCIH1755_LUNG", "NCIH1781_LUNG", "NCIH1792_LUNG", "NCIH1793_LUNG", 
  .     "NCIH1819_LUNG", "NCIH1836_LUNG", "NCIH1838_LUNG", "NCIH1869_LUNG", 
  .     "NCIH1876_LUNG", "NCIH1915_LUNG", "NCIH1930_LUNG", "NCIH1944_LUNG", 
  .     "NCIH1963_LUNG", "NCIH196_LUNG", "NCIH1975_LUNG", "NCIH2009_LUNG", 
  .     "NCIH2023_LUNG", "NCIH2029_LUNG", "NCIH2030_LUNG", "NCIH2052_PLEURA", 
  .     "NCIH2066_LUNG", "NCIH2073_LUNG", "NCIH2081_LUNG", "NCIH2085_LUNG", 
  .     "NCIH2087_LUNG", "NCIH209_LUNG", "NCIH2106_LUNG", "NCIH2110_LUNG", 
  .     "NCIH211_LUNG", "NCIH2122_LUNG", "NCIH2126_LUNG", "NCIH2170_LUNG", 
  .     "NCIH2171_LUNG", "NCIH2172_LUNG", "NCIH2196_LUNG", "NCIH2227_LUNG", 
  .     "NCIH2228_LUNG", "NCIH226_LUNG", "NCIH2286_LUNG", "NCIH2291_LUNG", 
  .     "NCIH2342_LUNG", "NCIH2347_LUNG", "NCIH23_LUNG", "NCIH2405_LUNG", 
  .     "NCIH2444_LUNG", "NCIH2452_PLEURA", "NCIH28_PLEURA", "NCIH292_LUNG", 
  .     "NCIH3122_LUNG", "NCIH322_LUNG", "NCIH3255_LUNG", "NCIH358_LUNG", 
  .     "NCIH441_LUNG", "NCIH446_LUNG", "NCIH460_LUNG", "NCIH508_LARGE_INTESTINE", 
  .     "NCIH510_LUNG", "NCIH520_LUNG", "NCIH522_LUNG", "NCIH524_LUNG", 
  .     "NCIH526_LUNG", "NCIH596_LUNG", "NCIH647_LUNG", "NCIH650_LUNG", 
  .     "NCIH660_PROSTATE", "NCIH661_LUNG", "NCIH684_LIVER", "NCIH69_LUNG", 
  .     "NCIH716_LARGE_INTESTINE", "NCIH727_LUNG", "NCIH747_LARGE_INTESTINE", 
  .     "NCIH810_LUNG", "NCIH82_LUNG", "NCIH838_LUNG", "NCIH841_LUNG", 
  .     "NCIH854_LUNG", "NCIH889_LUNG", "NCIH929_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "NCIN87_STOMACH", "NCO2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "NH6_AUTONOMIC_GANGLIA", "NHAHTDD_CENTRAL_NERVOUS_SYSTEM", 
  .     "NIHOVCAR3_OVARY", "NMCG1_CENTRAL_NERVOUS_SYSTEM", "NOMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "NUDHL1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "NUDUL1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "NUGC2_STOMACH", "NUGC3_STOMACH", "NUGC4_STOMACH", "OAW28_OVARY", 
  .     "OAW42_OVARY", "OC314_OVARY", "OCIMY5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "OCIMY7_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCIAML2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "OCIAML3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCIAML5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "OCILY19_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCILY3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "OCIM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCUM1_STOMACH", 
  .     "OE19_OESOPHAGUS", "OE21_OESOPHAGUS", "OE33_OESOPHAGUS", 
  .     "OELE_OVARY", "ONCODG1_OVARY", "ONS76_CENTRAL_NERVOUS_SYSTEM", 
  .     "OPM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "OSRC2_KIDNEY", "OUMS23_LARGE_INTESTINE", "OV56_OVARY", "OV7_OVARY", 
  .     "OV90_OVARY", "OVCAR4_OVARY", "OVCAR8_OVARY", "OVISE_OVARY", 
  .     "OVK18_OVARY", "OVKATE_OVARY", "OVMANA_OVARY", "OVSAHO_OVARY", 
  .     "OVTOKO_OVARY", "P12ICHIKAWA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "P31FUJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "PANC0203_PANCREAS", "PANC0213_PANCREAS", "PANC0327_PANCREAS", 
  .     "PANC0403_PANCREAS", "PANC0504_PANCREAS", "PANC0813_PANCREAS", 
  .     "PANC1005_PANCREAS", "PANC1_PANCREAS", "PATU8902_PANCREAS", 
  .     "PATU8988S_PANCREAS", "PATU8988T_PANCREAS", "PC9_LUNG", "PC14_LUNG", 
  .     "PC3_PROSTATE", "PECAPJ15_UPPER_AERODIGESTIVE_TRACT", "PECAPJ34CLONEC12_UPPER_AERODIGESTIVE_TRACT", 
  .     "PECAPJ41CLONED2_UPPER_AERODIGESTIVE_TRACT", "PECAPJ49_UPPER_AERODIGESTIVE_TRACT", 
  .     "PEER_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "PF382_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "PFEIFFER_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "PK1_PANCREAS", 
  .     "PK45H_PANCREAS", "PK59_PANCREAS", "PL21_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "PLB985_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "PLCPRF5_LIVER", 
  .     "PRECLH_PROSTATE", "PSN1_PANCREAS", "QGP1_PANCREAS", "RAJI_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "RCC10RGB_KIDNEY", "RCHACV_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "RCM1_LARGE_INTESTINE", "RDES_BONE", "RD_SOFT_TISSUE", "REC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "REH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "RERFGC1B_STOMACH", 
  .     "RERFLCAD1_LUNG", "RERFLCAD2_LUNG", "RERFLCAI_LUNG", "RERFLCKJ_LUNG", 
  .     "RERFLCMS_LUNG", "RERFLCSQ1_LUNG", "RH18_SOFT_TISSUE", "RH30_SOFT_TISSUE", 
  .     "RH41_SOFT_TISSUE", "RI1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "RKN_SOFT_TISSUE", "RKO_LARGE_INTESTINE", "RL952_ENDOMETRIUM", 
  .     "RL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "RMGI_OVARY", "RMUGS_OVARY", 
  .     "RPMI7951_SKIN", "RPMI8226_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "RPMI8402_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "RS411_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "RS5_FIBROBLAST", "RT112_URINARY_TRACT", "RT4_URINARY_TRACT", 
  .     "RVH421_SKIN", "S117_SOFT_TISSUE", "SALE_LUNG", "SAOS2_BONE", 
  .     "SBC5_LUNG", "SCABER_URINARY_TRACT", "SCC15_UPPER_AERODIGESTIVE_TRACT", 
  .     "SCC25_UPPER_AERODIGESTIVE_TRACT", "SCC4_UPPER_AERODIGESTIVE_TRACT", 
  .     "SCC9_UPPER_AERODIGESTIVE_TRACT", "SCLC21H_LUNG", "SEM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "SET2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SF126_CENTRAL_NERVOUS_SYSTEM", 
  .     "SF172_CENTRAL_NERVOUS_SYSTEM", "SF268_CENTRAL_NERVOUS_SYSTEM", 
  .     "SF295_CENTRAL_NERVOUS_SYSTEM", "SF539_CENTRAL_NERVOUS_SYSTEM", 
  .     "SF767_CENTRAL_NERVOUS_SYSTEM", "SH10TC_STOMACH", "SH4_SKIN", 
  .     "SHP77_LUNG", "SIGM5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "SIMA_AUTONOMIC_GANGLIA", "SJSA1_BONE", "SKBR3_BREAST", "SKCO1_LARGE_INTESTINE", 
  .     "SKES1_BONE", "SKHEP1_LIVER", "SKLMS1_SOFT_TISSUE", "SKLU1_LUNG", 
  .     "SKM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SKMEL1_SKIN", 
  .     "SKMEL24_SKIN", "SKMEL28_SKIN", "SKMEL30_SKIN", "SKMEL31_SKIN", 
  .     "SKMEL3_SKIN", "SKMEL5_SKIN", "SKMES1_LUNG", "SKMM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "SKNAS_AUTONOMIC_GANGLIA", "SKNBE2_AUTONOMIC_GANGLIA", "SKNDZ_AUTONOMIC_GANGLIA", 
  .     "SKNEP1_BONE", "SKNFI_AUTONOMIC_GANGLIA", "SKNMC_BONE", "SKNO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "SKNSH_AUTONOMIC_GANGLIA", "SKOV3_OVARY", "SKPNDW_BONE", 
  .     "SKRC20_KIDNEY", "SKUT1_SOFT_TISSUE", "SLR20_KIDNEY", "SLR21_KIDNEY", 
  .     "SLR23_KIDNEY", "SLR24_KIDNEY", "SLR25_KIDNEY", "SLR26_KIDNEY", 
  .     "SNB75_CENTRAL_NERVOUS_SYSTEM", "SNGM_ENDOMETRIUM", "SNU1033_LARGE_INTESTINE", 
  .     "SNU1040_LARGE_INTESTINE", "SNU1041_UPPER_AERODIGESTIVE_TRACT", 
  .     "SNU1066_UPPER_AERODIGESTIVE_TRACT", "SNU1076_UPPER_AERODIGESTIVE_TRACT", 
  .     "SNU1077_ENDOMETRIUM", "SNU1079_BILIARY_TRACT", "SNU1105_CENTRAL_NERVOUS_SYSTEM", 
  .     "SNU1196_BILIARY_TRACT", "SNU1197_LARGE_INTESTINE", "SNU119_OVARY", 
  .     "SNU1214_UPPER_AERODIGESTIVE_TRACT", "SNU1272_KIDNEY", "SNU16_STOMACH", 
  .     "SNU175_LARGE_INTESTINE", "SNU182_LIVER", "SNU1_STOMACH", 
  .     "SNU201_CENTRAL_NERVOUS_SYSTEM", "SNU213_PANCREAS", "SNU216_STOMACH", 
  .     "SNU245_BILIARY_TRACT", "SNU283_LARGE_INTESTINE", "SNU308_BILIARY_TRACT", 
  .     "SNU324_PANCREAS", "SNU349_KIDNEY", "SNU387_LIVER", "SNU398_LIVER", 
  .     "SNU407_LARGE_INTESTINE", "SNU410_PANCREAS", "SNU423_LIVER", 
  .     "SNU449_LIVER", "SNU466_CENTRAL_NERVOUS_SYSTEM", "SNU46_UPPER_AERODIGESTIVE_TRACT", 
  .     "SNU475_LIVER", "SNU478_BILIARY_TRACT", "SNU489_CENTRAL_NERVOUS_SYSTEM", 
  .     "SNU503_LARGE_INTESTINE", "SNU520_STOMACH", "SNU5_STOMACH", 
  .     "SNU601_STOMACH", "SNU61_LARGE_INTESTINE", "SNU620_STOMACH", 
  .     "SNU626_CENTRAL_NERVOUS_SYSTEM", "SNU668_STOMACH", "SNU685_ENDOMETRIUM", 
  .     "SNU719_STOMACH", "SNU738_CENTRAL_NERVOUS_SYSTEM", "SNU761_LIVER", 
  .     "SNU81_LARGE_INTESTINE", "SNU840_OVARY", "SNU869_BILIARY_TRACT", 
  .     "SNU878_LIVER", "SNU886_LIVER", "SNU899_UPPER_AERODIGESTIVE_TRACT", 
  .     "SNU8_OVARY", "SNUC1_LARGE_INTESTINE", "SNUC2A_LARGE_INTESTINE", 
  .     "SNUC4_LARGE_INTESTINE", "SNUC5_LARGE_INTESTINE", "SQ1_LUNG", 
  .     "SR786_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "ST486_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "STM9101_SOFT_TISSUE", "SU8686_PANCREAS", "SUDHL10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "SUDHL1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUDHL4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "SUDHL5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUDHL6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "SUDHL8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUIT2_PANCREAS", 
  .     "SUPB15_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "SUPT11_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUPT1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "SW1088_CENTRAL_NERVOUS_SYSTEM", "SW1116_LARGE_INTESTINE", 
  .     "SW1271_LUNG", "SW1353_BONE", "SW1417_LARGE_INTESTINE", "SW1463_LARGE_INTESTINE", 
  .     "SW1573_LUNG", "SW1710_URINARY_TRACT", "SW1783_CENTRAL_NERVOUS_SYSTEM", 
  .     "SW1990_PANCREAS", "SW403_LARGE_INTESTINE", "SW480_LARGE_INTESTINE", 
  .     "SW48_LARGE_INTESTINE", "SW579_THYROID", "SW620_LARGE_INTESTINE", 
  .     "SW780_URINARY_TRACT", "SW837_LARGE_INTESTINE", "SW900_LUNG", 
  .     "SW948_LARGE_INTESTINE", "SIHA_CERVIX", "T173_FIBROBLAST", 
  .     "T24_URINARY_TRACT", "T3M10_LUNG", "T3M4_PANCREAS", "T47D_BREAST", 
  .     "T84_LARGE_INTESTINE", "T98G_CENTRAL_NERVOUS_SYSTEM", "TALL1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "TC71_BONE", "TCCPAN2_PANCREAS", "TCCSUP_URINARY_TRACT", 
  .     "TE10_OESOPHAGUS", "TE11_OESOPHAGUS", "TE125T_FIBROBLAST", 
  .     "TE14_OESOPHAGUS", "TE159T_FIBROBLAST", "TE15_OESOPHAGUS", 
  .     "TE1_OESOPHAGUS", "TE441T_SOFT_TISSUE", "TE4_OESOPHAGUS", 
  .     "TE5_OESOPHAGUS", "TE617T_SOFT_TISSUE", "TE6_OESOPHAGUS", 
  .     "TE8_OESOPHAGUS", "TE9_OESOPHAGUS", "TEN_ENDOMETRIUM", "TF1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "TGBC11TKB_STOMACH", "THP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "TIG3TD_FIBROBLAST", "TM87_SOFT_TISSUE", "TM31_CENTRAL_NERVOUS_SYSTEM", 
  .     "TO175T_FIBROBLAST", "TOLEDO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "TOV112D_OVARY", "TOV21G_OVARY", "TT2609C02_THYROID", "TTC709_SOFT_TISSUE", 
  .     "TTC1240_SOFT_TISSUE", "TTC549_SOFT_TISSUE", "TTC642_SOFT_TISSUE", 
  .     "TT_OESOPHAGUS", "TT_THYROID", "TUHR10TKB_KIDNEY", "TUHR14TKB_KIDNEY", 
  .     "TUHR4TKB_KIDNEY", "TYKNU_OVARY", "U118MG_CENTRAL_NERVOUS_SYSTEM", 
  .     "U178_CENTRAL_NERVOUS_SYSTEM", "U251MG_CENTRAL_NERVOUS_SYSTEM", 
  .     "U266B1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "U2OS_BONE", 
  .     "U343_CENTRAL_NERVOUS_SYSTEM", "U87MG_CENTRAL_NERVOUS_SYSTEM", 
  .     "U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "UACC257_SKIN", 
  .     "UACC62_SKIN", "UACC812_BREAST", "UACC893_BREAST", "UBLC1_URINARY_TRACT", 
  .     "UMUC1_URINARY_TRACT", "UMUC3_URINARY_TRACT", "UO31_KIDNEY", 
  .     "UOK101_KIDNEY", "VCAP_PROSTATE", "VMCUB1_URINARY_TRACT", 
  .     "VMRCRCW_KIDNEY", "VMRCRCZ_KIDNEY", "WM115_SKIN", "WM1799_SKIN", 
  .     "WM2664_SKIN", "WM793_SKIN", "WM88_SKIN", "WM983B_SKIN", 
  .     "WSUDLCL2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "YAPC_PANCREAS", 
  .     "YD10B_UPPER_AERODIGESTIVE_TRACT", "YD15_SALIVARY_GLAND", 
  .     "YD38_UPPER_AERODIGESTIVE_TRACT", "YD8_UPPER_AERODIGESTIVE_TRACT", 
  .     "YH13_CENTRAL_NERVOUS_SYSTEM", "YKG1_CENTRAL_NERVOUS_SYSTEM", 
  .     "ZR751_BREAST", "ZR7530_BREAST", "143B_BONE", "BIN67_OVARY", 
  .     "CHLA15_AUTONOMIC_GANGLIA", "CHLA266_SOFT_TISSUE", "COGAR359_SOFT_TISSUE", 
  .     "COGE352_BONE", "COGN278_AUTONOMIC_GANGLIA", "COGN305_AUTONOMIC_GANGLIA", 
  .     "COV504_OVARY", "CW9019_SOFT_TISSUE", "D425_CENTRAL_NERVOUS_SYSTEM", 
  .     "D458_CENTRAL_NERVOUS_SYSTEM", "DLD1_LARGE_INTESTINE", "F5_CENTRAL_NERVOUS_SYSTEM", 
  .     "JR_SOFT_TISSUE", "L82_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "MAC2A_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MYLA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "PEDS005TPFAD_KIDNEY", "PEDS015T_SOFT_TISSUE", "RT11284_URINARY_TRACT", 
  .     "SMSCTR_SOFT_TISSUE", "SMZ1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", 
  .     "SW982_SOFT_TISSUE", "SYO1_SOFT_TISSUE", "TC138_BONE", "TC205_BONE", 
  .     "UPCISCC152_UPPER_AERODIGESTIVE_TRACT", "UW228_CENTRAL_NERVOUS_SYSTEM"
  .     ), subscript_action = NULL, subscript_arg = "select_cl", 
  .     rlang = list(inherit = TRUE), call = dplyr::select(., c("Description", 
  .         select_cl))), class = c("vctrs_error_subscript_oob", 
  . "vctrs_error_subscript", "rlang_error", "error", "condition")))
28. handlers[[1L]](cnd)
29. cnd_signal(cnd)
30. signal_abort(cnd)
In [261]:
CCLE_exp
A data.table: 56318 × 1050
NameDescription22RV1_PROSTATE2313287_STOMACH253JBV_URINARY_TRACT253J_URINARY_TRACT42MGBA_CENTRAL_NERVOUS_SYSTEM5637_URINARY_TRACT59M_OVARY639V_URINARY_TRACT⋯PEDS015T_SOFT_TISSUERT11284_URINARY_TRACTSMSCTR_SOFT_TISSUESMZ1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUESW982_SOFT_TISSUESYO1_SOFT_TISSUETC138_BONETC205_BONEUPCISCC152_UPPER_AERODIGESTIVE_TRACTUW228_CENTRAL_NERVOUS_SYSTEM
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⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋱⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
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In [262]:
sum(select_cl %in% CCLE_info$cell_line)
657
In [263]:
CCLE_merge_info <- merge(CCLE_info,CCLE_drug,by.x = 'cell_line',by.y='CCLE Cell Line Name',all = F)
In [264]:
select_cl <- CCLE_merge_info %>% 
    filter(Target == 'EGFR') %>% 
    filter(tumor_type == 'Lung')

select_cl_name <-  select_cl%>%
    dplyr::select('cell_line') %>% 
    t() %>% c()
In [265]:
EGFR_drug_exp <- CCLE_exp %>% 
    dplyr::select(c('Description',select_cl_name)) %>%
    dplyr::filter(Description %in% drivers$V1) %>% 
    as.data.frame()
Warning message:
“Using an external vector in selections was deprecated in tidyselect 1.1.0.
ℹ Please use `all_of()` or `any_of()` instead.
  # Was:
  data %>% select(select_cl_name)

  # Now:
  data %>% select(all_of(select_cl_name))

See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.”
In [266]:
EGFR_drug_exp
A data.frame: 25 × 32
DescriptionA549_LUNGCALU6_LUNGHARA_LUNGHCC15_LUNGHCC827_LUNGKNS62_LUNGLCLC103H_LUNGLUDLU1_LUNGMORCPR_LUNG⋯NCIH2170_LUNGNCIH2172_LUNGNCIH23_LUNGNCIH322_LUNGNCIH441_LUNGNCIH460_LUNGNCIH520_LUNGPC14_LUNGRERFLCAI_LUNGSKMES1_LUNG
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
GTF2B 9.42187 9.52591 9.82747 15.05349 20.98313 7.69904 12.62830 5.32157 8.86021⋯ 10.34348 10.29332 12.15199 12.84523 15.50471 9.35671 8.28753 11.64325 9.09639 17.61231
MSH6 30.21349 16.66706 27.83179 22.35493 13.36039 6.35602 11.47562 15.91921 16.38052⋯ 14.65205 13.84182 40.61627 16.97435 28.10175 20.41480 19.13857 20.83116 19.96815 17.13223
MYO1B 84.16625 12.21347 56.12199 21.82845 53.94776 29.39360 15.13058 24.97976 6.97019⋯ 22.31954 31.88694 9.43251 21.79440 27.86195 18.75975 14.30759 22.58268 19.45658 26.52773
BARD1 3.99177 3.68420 6.96785 6.21935 3.84172 2.16954 2.65518 3.98600 3.47008⋯ 6.01418 5.08219 5.91497 4.53696 12.00526 4.24700 5.60684 3.35062 8.43791 4.30252
PLK2 106.71158 13.84899 22.49375 42.81476 80.30707 8.90575 36.64318 30.06351 5.89568⋯ 6.77429 64.54214 36.31806 24.04011 3.34105 36.31531 0.64743 96.33008104.66184469.22522
HIST1H1C 21.23154 14.60151 28.73901 13.71157 14.47164 14.01782 32.72435 31.40698 47.66726⋯ 86.00359131.69919 31.08146 38.50328 30.73631 7.50665 4.84441 20.67738 11.63652 26.83206
CEBPD 1.38818 6.02801 2.71927 2.79843 4.16668 2.49031 1.67320 12.26365 18.25912⋯ 2.76244 11.68271 2.57368 4.38601 18.05979 2.19177 1.65240 2.91365 2.93413 7.44752
TRIB1 5.72782 7.86291 2.70509 3.31164 5.78330 8.91243 6.38144 5.62817 15.39088⋯ 6.41827 4.81647 6.84887 6.46973 8.90075 1.51547 3.06501 22.61054 1.08468 4.81291
TUBB4B 231.44135384.12991 374.27832210.75525 534.51093 78.32369217.06387296.64926275.04034⋯216.19600387.98401 263.86459639.10437144.75571 319.47800158.35429434.68204427.34421191.79034
CD9 46.91733 38.28646 31.07783 46.73436 95.13908149.23459107.34277149.40462126.39982⋯ 79.27206 19.99291 60.11135 85.53854218.80959 14.52904 13.20242116.46647100.11279 55.82069
TUBA1B 1251.16431831.361081064.56189953.215761235.05884383.25098701.26965871.77478689.48291⋯604.80829902.410221239.90430935.79065624.926761880.50012809.44269680.46375970.42969739.64038
KLF5 15.03708 10.21180 100.12168 12.46294 15.59129 32.08125 2.54261 78.08482 23.68784⋯ 94.37977 9.38432 4.42372 21.59323 23.53358 4.31649 20.66944 18.11410 3.96864 2.15124
LMO7 3.98502 5.73257 6.22587 12.95094 11.45193 13.01007 1.74905 7.06538 9.95970⋯ 30.66059 1.12792 3.76256 12.30936 51.01361 0.84987 16.34881 12.48705 25.55365 9.25490
TFDP1 33.28401 35.17064 58.74771 27.59179 38.70246 62.78336 16.84071 65.83632 32.46316⋯101.95407 30.29656 43.15556 72.90529 40.46710 45.88659 36.02008 49.30787 56.62517 40.42910
DAAM1 2.93813 2.59296 3.76573 1.97393 5.56168 6.13586 10.96501 4.14269 3.62285⋯ 6.79304 8.33280 3.35902 10.16890 8.90246 0.50112 2.86945 12.34564 1.35333 7.29355
HSPA2 35.52928 8.29720 7.73832 9.07434 1.30731 4.76532 2.08593 5.46101 2.72771⋯ 4.09173132.31943 9.44749 5.66068 1.13168 3.74013 0.96668 5.14966 9.94315 3.41503
ANXA2 905.70850297.84131 292.30426351.07166 491.56302350.67047336.53204373.70554169.90025⋯251.64005207.98903 167.41335361.98868219.16507 298.56116103.98492411.26993349.70312898.98889
TPM1 207.13792109.33822 6.01246 7.75077 77.59810 13.70145 64.97376 11.77554 5.30921⋯ 1.00032 75.56107 53.34697 46.38272 3.00344 6.30246 6.22729 31.33055 67.62692 48.19003
DGKE 1.56391 1.23536 2.69828 2.16853 2.27285 2.91677 0.99521 2.08815 0.38799⋯ 3.56795 1.30310 0.49816 0.10916 3.42770 2.08906 2.69382 2.14484 0.07427 3.55946
RAB31 6.68595 15.51748 6.88650 15.44389 8.22771 4.91694 67.34886 4.78308 4.37403⋯ 0.35001 10.03424 14.98078 2.07566 6.98250 6.76498 0.44960 14.65951 33.87274 21.38955
PSMC4 173.47475 84.36823 78.21808 79.54345 66.41516 56.39879 71.49141 80.96620 47.68212⋯111.76073157.40776 115.40165 94.40004 31.04118 94.04908 77.77753 68.05058 57.50246 65.02472
SLC1A5 105.83581 74.95303 61.44871216.71173 28.60874 79.93523 65.07303105.79476172.34282⋯181.72800 38.73779 58.71933 63.96908 83.75444 89.03591213.62213 80.72714 96.84824 37.72397
RRBP1 29.36313 12.82536 26.62716 25.52167 42.59126 42.19027 38.68987 85.32716 48.83669⋯ 25.93232 31.93937 20.28370 24.85145 32.55831 37.32225 6.98978 58.63967 17.31573 49.29018
TGM2 70.67321 55.75968 0.89151247.90877 40.08327 1.05652 48.85313 4.81757 7.18035⋯ 4.96301 77.50578 44.42159 83.18359 38.66368 2.08099 0.31467169.11215126.63876102.45492
TFAP2C 8.42173 6.23058 3.42565 7.07467 2.30976 6.71867 2.96221 15.25246 21.67481⋯ 5.78693 10.74191 35.81564 23.67968 7.76593 3.17164 0.08243 8.70646 1.77203 2.53308
In [267]:
#利用aggregate函数,对相同的基因名按列取平均
EGFR_drug_exp <- aggregate(.~Description,mean,data=EGFR_drug_exp)
In [268]:
rn <- EGFR_drug_exp$Description
EGFR_drug_exp <- EGFR_drug_exp[,2:ncol(EGFR_drug_exp)]
rownames(EGFR_drug_exp) <- rn
EGFR_drug_exp
A data.frame: 25 × 31
A549_LUNGCALU6_LUNGHARA_LUNGHCC15_LUNGHCC827_LUNGKNS62_LUNGLCLC103H_LUNGLUDLU1_LUNGMORCPR_LUNGNCIH1299_LUNG⋯NCIH2170_LUNGNCIH2172_LUNGNCIH23_LUNGNCIH322_LUNGNCIH441_LUNGNCIH460_LUNGNCIH520_LUNGPC14_LUNGRERFLCAI_LUNGSKMES1_LUNG
<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
ANXA2 905.70850297.84131 292.30426351.07166 491.56302350.67047336.53204373.70554169.90025 165.48709⋯251.64005207.98903 167.41335361.98868219.16507 298.56116103.98492411.26993349.70312898.98889
BARD1 3.99177 3.68420 6.96785 6.21935 3.84172 2.16954 2.65518 3.98600 3.47008 4.46638⋯ 6.01418 5.08219 5.91497 4.53696 12.00526 4.24700 5.60684 3.35062 8.43791 4.30252
CD9 46.91733 38.28646 31.07783 46.73436 95.13908149.23459107.34277149.40462126.39982 64.88426⋯ 79.27206 19.99291 60.11135 85.53854218.80959 14.52904 13.20242116.46647100.11279 55.82069
CEBPD 1.38818 6.02801 2.71927 2.79843 4.16668 2.49031 1.67320 12.26365 18.25912 2.75232⋯ 2.76244 11.68271 2.57368 4.38601 18.05979 2.19177 1.65240 2.91365 2.93413 7.44752
DAAM1 2.93813 2.59296 3.76573 1.97393 5.56168 6.13586 10.96501 4.14269 3.62285 1.69593⋯ 6.79304 8.33280 3.35902 10.16890 8.90246 0.50112 2.86945 12.34564 1.35333 7.29355
DGKE 1.56391 1.23536 2.69828 2.16853 2.27285 2.91677 0.99521 2.08815 0.38799 3.28413⋯ 3.56795 1.30310 0.49816 0.10916 3.42770 2.08906 2.69382 2.14484 0.07427 3.55946
GTF2B 9.42187 9.52591 9.82747 15.05349 20.98313 7.69904 12.62830 5.32157 8.86021 12.71671⋯ 10.34348 10.29332 12.15199 12.84523 15.50471 9.35671 8.28753 11.64325 9.09639 17.61231
HIST1H1C 21.23154 14.60151 28.73901 13.71157 14.47164 14.01782 32.72435 31.40698 47.66726 25.25327⋯ 86.00359131.69919 31.08146 38.50328 30.73631 7.50665 4.84441 20.67738 11.63652 26.83206
HSPA2 35.52928 8.29720 7.73832 9.07434 1.30731 4.76532 2.08593 5.46101 2.72771 7.59667⋯ 4.09173132.31943 9.44749 5.66068 1.13168 3.74013 0.96668 5.14966 9.94315 3.41503
KLF5 15.03708 10.21180 100.12168 12.46294 15.59129 32.08125 2.54261 78.08482 23.68784 3.95696⋯ 94.37977 9.38432 4.42372 21.59323 23.53358 4.31649 20.66944 18.11410 3.96864 2.15124
LMO7 3.98502 5.73257 6.22587 12.95094 11.45193 13.01007 1.74905 7.06538 9.95970 4.04487⋯ 30.66059 1.12792 3.76256 12.30936 51.01361 0.84987 16.34881 12.48705 25.55365 9.25490
MSH6 30.21349 16.66706 27.83179 22.35493 13.36039 6.35602 11.47562 15.91921 16.38052 22.75378⋯ 14.65205 13.84182 40.61627 16.97435 28.10175 20.41480 19.13857 20.83116 19.96815 17.13223
MYO1B 84.16625 12.21347 56.12199 21.82845 53.94776 29.39360 15.13058 24.97976 6.97019 9.03299⋯ 22.31954 31.88694 9.43251 21.79440 27.86195 18.75975 14.30759 22.58268 19.45658 26.52773
PLK2 106.71158 13.84899 22.49375 42.81476 80.30707 8.90575 36.64318 30.06351 5.89568 24.46885⋯ 6.77429 64.54214 36.31806 24.04011 3.34105 36.31531 0.64743 96.33008104.66184469.22522
PSMC4 173.47475 84.36823 78.21808 79.54345 66.41516 56.39879 71.49141 80.96620 47.68212 195.19131⋯111.76073157.40776 115.40165 94.40004 31.04118 94.04908 77.77753 68.05058 57.50246 65.02472
RAB31 6.68595 15.51748 6.88650 15.44389 8.22771 4.91694 67.34886 4.78308 4.37403 18.54549⋯ 0.35001 10.03424 14.98078 2.07566 6.98250 6.76498 0.44960 14.65951 33.87274 21.38955
RRBP1 29.36313 12.82536 26.62716 25.52167 42.59126 42.19027 38.68987 85.32716 48.83669 26.05423⋯ 25.93232 31.93937 20.28370 24.85145 32.55831 37.32225 6.98978 58.63967 17.31573 49.29018
SLC1A5 105.83581 74.95303 61.44871216.71173 28.60874 79.93523 65.07303105.79476172.34282 47.80180⋯181.72800 38.73779 58.71933 63.96908 83.75444 89.03591213.62213 80.72714 96.84824 37.72397
TFAP2C 8.42173 6.23058 3.42565 7.07467 2.30976 6.71867 2.96221 15.25246 21.67481 0.17790⋯ 5.78693 10.74191 35.81564 23.67968 7.76593 3.17164 0.08243 8.70646 1.77203 2.53308
TFDP1 33.28401 35.17064 58.74771 27.59179 38.70246 62.78336 16.84071 65.83632 32.46316 28.33863⋯101.95407 30.29656 43.15556 72.90529 40.46710 45.88659 36.02008 49.30787 56.62517 40.42910
TGM2 70.67321 55.75968 0.89151247.90877 40.08327 1.05652 48.85313 4.81757 7.18035 101.08371⋯ 4.96301 77.50578 44.42159 83.18359 38.66368 2.08099 0.31467169.11215126.63876102.45492
TPM1 207.13792109.33822 6.01246 7.75077 77.59810 13.70145 64.97376 11.77554 5.30921 44.48190⋯ 1.00032 75.56107 53.34697 46.38272 3.00344 6.30246 6.22729 31.33055 67.62692 48.19003
TRIB1 5.72782 7.86291 2.70509 3.31164 5.78330 8.91243 6.38144 5.62817 15.39088 6.49076⋯ 6.41827 4.81647 6.84887 6.46973 8.90075 1.51547 3.06501 22.61054 1.08468 4.81291
TUBA1B1251.16431831.361081064.56189953.215761235.05884383.25098701.26965871.77478689.482911067.26819⋯604.80829902.410221239.90430935.79065624.926761880.50012809.44269680.46375970.42969739.64038
TUBB4B 231.44135384.12991 374.27832210.75525 534.51093 78.32369217.06387296.64926275.04034 328.42111⋯216.19600387.98401 263.86459639.10437144.75571 319.47800158.35429434.68204427.34421191.79034
In [269]:
EGFR_drug_exp <- log2(EGFR_drug_exp)
In [270]:
EGFR_drug_exp
A data.frame: 25 × 31
A549_LUNGCALU6_LUNGHARA_LUNGHCC15_LUNGHCC827_LUNGKNS62_LUNGLCLC103H_LUNGLUDLU1_LUNGMORCPR_LUNGNCIH1299_LUNG⋯NCIH2170_LUNGNCIH2172_LUNGNCIH23_LUNGNCIH322_LUNGNCIH441_LUNGNCIH460_LUNGNCIH520_LUNGPC14_LUNGRERFLCAI_LUNGSKMES1_LUNG
<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
ANXA2 9.82290308.2184001 8.19132708.4556217 8.94123268.45397214 8.3946000558.545758 7.408544 7.3705749⋯ 7.97521774377.7003636 7.387271 8.4998017.7758741 8.2218827 6.70023058.683942 8.44998699.812159
BARD1 1.99702861.8813514 2.80071362.6367638 1.94175241.11738919 1.4088096681.994942 1.794969 2.1591060⋯ 2.58836804742.3454503 2.564371 2.1817263.5855947 2.0864441 2.48718791.744428 3.07688572.105182
CD9 5.55204905.2587624 4.95781395.5464117 6.57196627.22143816 6.7460812127.223081 6.981851 6.0197966⋯ 6.30874056234.3214166 5.909566 6.4185037.7735322 3.8608675 3.72273056.863771 6.64548255.802728
CEBPD 0.47319462.5916818 1.44321941.4846177 2.05889831.31632534 0.7426099033.616317 4.190545 1.4606482⋯ 1.46594312963.5463031 1.363833 2.1329094.1747092 1.1320964 0.72456301.542828 1.55293282.896760
DAAM1 1.55489821.3746000 1.91292960.9810708 2.47552072.61726557 3.4548352232.050568 1.857125 0.7620766⋯ 2.76405734933.0588014 1.748040 3.3460923.1542040-0.9967720 1.52077423.625930 0.43651372.866621
DGKE 0.64515750.3049315 1.43204011.1167174 1.18450251.54437163-0.0069271131.062225-1.365909 1.7155112⋯ 1.83509539800.3819478-1.005319-3.1954841.7772408 1.0628539 1.42965351.100870-3.75107661.831658
GTF2B 3.23601343.2518569 3.29682013.9120261 4.39115802.94467857 3.6585885342.411852 3.147341 3.6686536⋯ 3.37064974813.3636365 3.603121 3.6831613.9546346 3.2260013 3.05094223.541422 3.18529414.138512
HIST1H1C 4.40813713.8680457 4.84493853.7773219 3.85515653.80919010 5.0322926314.973013 5.574927 4.6583983⋯ 6.42632497767.0411027 4.957982 5.2669094.9418721 2.9081692 2.27632104.369981 3.54058784.745886
HSPA2 5.15093653.0526246 2.95202043.1817927 0.38660132.25257310 1.0606907442.449168 1.447690 2.9253672⋯ 2.03271094957.0478811 3.239931 2.5009750.1784661 1.9030884-0.04888972.364477 3.31370301.771898
KLF5 3.91045253.3521653 6.64561063.6395725 3.96266845.00365845 1.3463101906.286970 4.566075 1.9843925⋯ 6.56040575063.2302522 2.145260 4.4325074.5566489 2.1098586 4.36942744.179041 1.98864471.105168
LMO7 1.99458702.5191821 2.63827553.6949849 3.51751893.70155682 0.8065715322.820767 3.316102 2.0160933⋯ 4.93831355390.1736647 1.911715 3.6216845.6728103-0.2346859 4.03111373.642361 4.67545753.210217
MSH6 4.91712094.0589277 4.79866184.4825211 3.73989022.66812366 3.5205001963.992697 4.033909 4.5080343⋯ 3.87303062423.7909617 5.343986 4.0852844.8125881 4.3515435 4.25841114.380671 4.31962884.098641
MYO1B 6.39516993.6104012 5.81049434.4481378 5.75349124.87743016 3.9193953864.642688 2.801198 3.1752036⋯ 4.48023538884.9948938 3.237642 4.4458864.8002243 4.2295687 3.83870884.497145 4.28218624.729429
PLK2 6.73757293.7917089 4.49145235.4200363 6.32745513.15473711 5.1954728054.909942 2.559658 4.6128744⋯ 2.76006974846.0121695 5.182615 4.5873721.7403016 5.1825060-0.62720396.589914 6.70959178.874137
PSMC4 7.43858196.3986279 6.28943026.3136712 6.05344075.81759231 6.1596980016.339248 5.575376 7.6087450⋯ 6.80426953917.2983629 6.850520 6.5607164.9561115 6.5553419 6.28128156.088536 5.84555186.022916
RAB31 2.74113263.9558224 2.78377093.9489643 3.04049092.29776075 6.0735816212.257940 2.128963 4.2129965⋯-1.51453195363.3268594 3.905041 1.0535702.8037437 2.7580857-1.15328613.873765 5.08205284.418834
RRBP1 4.87593393.6809274 4.73482674.6736508 5.41248555.39883842 5.2738839766.414933 5.609894 4.7034457⋯ 4.69667937564.9972640 4.342249 4.6352585.0249539 5.2219641 2.80524705.873805 4.11401135.623228
SLC1A5 6.72568406.2279149 5.94131087.7596334 4.83838416.32075958 6.0239878266.725124 7.429137 5.5789930⋯ 7.50563691185.2756697 5.875764 5.9993036.3880938 6.4763154 7.73891736.334982 6.59765395.237410
TFAP2C 3.07411662.6393665 1.77637782.8226629 1.20774302.74817567 1.5666739213.930970 4.437947-2.4908616⋯ 2.53279819313.4251786 5.162518 4.5655782.9571587 1.6652290-3.60068673.122086 0.82540301.340893
TFDP1 5.05675745.1362997 5.87646074.7861671 5.27435345.97231034 4.0738810586.040812 5.020732 4.8246981⋯ 6.67177555864.9210821 5.431475 6.1879525.3386776 5.5200007 5.17072955.623746 5.82337165.337322
TGM2 6.14309155.8011504-0.16567717.9536655 5.32492830.07932008 5.6103790932.268306 2.844054 6.6594067⋯ 2.31121536156.2762320 5.473189 6.3782275.2729071 1.0572700-1.66808857.401837 6.98457526.678845
TPM1 7.69444796.7726540 2.58795542.9543396 6.27794943.77625667 6.0217852913.557721 2.408497 5.4751465⋯ 0.00046158866.2395712 5.737334 5.5355161.5866158 2.6559151 2.63860454.969498 6.07952575.590663
TRIB1 2.51798622.9750633 1.43567661.7275458 2.53189293.15581884 2.6738820112.492666 3.944004 2.6983874⋯ 2.68218448132.2679762 2.775866 2.6937063.1539269 0.5997653 1.61589184.498924 0.11726952.266909
TUBA1B10.28905559.699331410.05604419.896659010.27036418.58214567 9.4538254819.767812 9.42937110.0597070⋯ 9.24033410429.817639610.276013 9.8700429.287543310.8769007 9.66078519.410374 9.92247999.530680
TUBB4B 7.85450288.5854505 8.54796777.7194248 9.06207566.29137683 7.7619758018.212614 8.103499 8.3594031⋯ 7.75619602078.5998534 8.043654 9.3199087.1774764 8.3195728 7.30701218.763817 8.73925487.583386
In [271]:
EGFR_drug_exp$V1 = rownames(EGFR_drug_exp)
In [272]:
score_df <- merge(EGFR_drug_exp,drivers,by = 'V1',all = F)
score_df
A data.frame: 25 × 44
V1A549_LUNGCALU6_LUNGHARA_LUNGHCC15_LUNGHCC827_LUNGKNS62_LUNGLCLC103H_LUNGLUDLU1_LUNGMORCPR_LUNG⋯weight_grad_total_dir_meancountsis_tfis_in_FAMis_in_ROSis_in_Pathwayrank_shap_weightrank_grad_weightdirectionsdirections_cal
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><int><lgl><lgl><lgl><lgl><dbl><dbl><lgl><dbl>
ANXA2 9.82290308.2184001 8.19132708.4556217 8.94123268.45397214 8.3946000558.545758 7.408544⋯-7.150776e-05 9FALSEFALSEFALSEFALSE 814FALSE-1
BARD1 1.99702861.8813514 2.80071362.6367638 1.94175241.11738919 1.4088096681.994942 1.794969⋯ 1.084314e-0410FALSEFALSEFALSEFALSE 2 7 TRUE 1
CD9 5.55204905.2587624 4.95781395.5464117 6.57196627.22143816 6.7460812127.223081 6.981851⋯-1.463574e-04 6FALSEFALSEFALSEFALSE 3 4FALSE-1
CEBPD 0.47319462.5916818 1.44321941.4846177 2.05889831.31632534 0.7426099033.616317 4.190545⋯ 7.729752e-05 9 TRUEFALSEFALSEFALSE2011 TRUE 1
DAAM1 1.55489821.3746000 1.91292960.9810708 2.47552072.61726557 3.4548352232.050568 1.857125⋯ 1.041791e-04 4FALSEFALSEFALSEFALSE 410 TRUE 1
DGKE 0.64515750.3049315 1.43204011.1167174 1.18450251.54437163-0.0069271131.062225-1.365909⋯-5.831443e-05 9FALSEFALSEFALSEFALSE1317FALSE-1
GTF2B 3.23601343.2518569 3.29682013.9120261 4.39115802.94467857 3.6585885342.411852 3.147341⋯-1.316672e-0410 TRUEFALSEFALSEFALSE11 6FALSE-1
HIST1H1C 4.40813713.8680457 4.84493853.7773219 3.85515653.80919010 5.0322926314.973013 5.574927⋯-7.409843e-05 4FALSEFALSEFALSEFALSE2313FALSE-1
HSPA2 5.15093653.0526246 2.95202043.1817927 0.38660132.25257310 1.0606907442.449168 1.447690⋯-2.506919e-04 8FALSEFALSEFALSEFALSE17 1FALSE-1
KLF5 3.91045253.3521653 6.64561063.6395725 3.96266845.00365845 1.3463101906.286970 4.566075⋯ 1.071805e-04 6 TRUEFALSEFALSEFALSE24 9 TRUE 1
LMO7 1.99458702.5191821 2.63827553.6949849 3.51751893.70155682 0.8065715322.820767 3.316102⋯ 2.352714e-04 6FALSEFALSEFALSEFALSE21 2 TRUE 1
MSH6 4.91712094.0589277 4.79866184.4825211 3.73989022.66812366 3.5205001963.992697 4.033909⋯ 7.425508e-0510FALSEFALSEFALSEFALSE 612 TRUE 1
MYO1B 6.39516993.6104012 5.81049434.4481378 5.75349124.87743016 3.9193953864.642688 2.801198⋯ 1.748662e-0410FALSEFALSEFALSEFALSE12 3 TRUE 1
PLK2 6.73757293.7917089 4.49145235.4200363 6.32745513.15473711 5.1954728054.909942 2.559658⋯ 6.720297e-05 9FALSEFALSEFALSEFALSE1015 TRUE 1
PSMC4 7.43858196.3986279 6.28943026.3136712 6.05344075.81759231 6.1596980016.339248 5.575376⋯-4.451585e-05 9FALSEFALSEFALSEFALSE1420FALSE-1
RAB31 2.74113263.9558224 2.78377093.9489643 3.04049092.29776075 6.0735816212.257940 2.128963⋯-5.959134e-05 8FALSEFALSEFALSEFALSE 916FALSE-1
RRBP1 4.87593393.6809274 4.73482674.6736508 5.41248555.39883842 5.2738839766.414933 5.609894⋯-1.221697e-05 9FALSEFALSEFALSEFALSE1823FALSE-1
SLC1A5 6.72568406.2279149 5.94131087.7596334 4.83838416.32075958 6.0239878266.725124 7.429137⋯ 1.082030e-0410FALSEFALSEFALSEFALSE 1 8 TRUE 1
TFAP2C 3.07411662.6393665 1.77637782.8226629 1.20774302.74817567 1.5666739213.930970 4.437947⋯ 1.130995e-05 7 TRUEFALSEFALSEFALSE2625 TRUE 1
TFDP1 5.05675745.1362997 5.87646074.7861671 5.27435345.97231034 4.0738810586.040812 5.020732⋯-5.785423e-0510 TRUEFALSEFALSEFALSE 718FALSE-1
TGM2 6.14309155.8011504-0.16567717.9536655 5.32492830.07932008 5.6103790932.268306 2.844054⋯ 5.989809e-0610FALSEFALSEFALSEFALSE1626 TRUE 1
TPM1 7.69444796.7726540 2.58795542.9543396 6.27794943.77625667 6.0217852913.557721 2.408497⋯ 2.991494e-05 6FALSEFALSEFALSEFALSE2521 TRUE 1
TRIB1 2.51798622.9750633 1.43567661.7275458 2.53189293.15581884 2.6738820112.492666 3.944004⋯ 2.118026e-05 6FALSEFALSEFALSEFALSE2222 TRUE 1
TUBA1B 10.28905559.699331410.05604419.896659010.27036418.58214567 9.4538254819.767812 9.429371⋯-1.412048e-0410FALSEFALSEFALSEFALSE 5 5FALSE-1
TUBB4B 7.85450288.5854505 8.54796777.7194248 9.06207566.29137683 7.7619758018.212614 8.103499⋯ 1.148352e-0510FALSEFALSEFALSEFALSE1924 TRUE 1
In [273]:
FUN1 <- function(x){
    sum(x*score_df$weight_shap_total_mean)
    #sum(x)
}
score <- apply(score_df[,2:32],MARGIN  = 2,FUN = FUN1)
In [274]:
score
A549_LUNG
0.134837478227318
CALU6_LUNG
0.120836389809897
HARA_LUNG
0.1230817780117
HCC15_LUNG
0.130704018944912
HCC827_LUNG
0.129378238192507
KNS62_LUNG
0.116708334606072
LCLC103H_LUNG
0.124359672248607
LUDLU1_LUNG
0.129448281875588
MORCPR_LUNG
0.11897644840739
NCIH1299_LUNG
0.121988442361029
NCIH1581_LUNG
0.0887077652188179
NCIH1648_LUNG
0.133232983835933
NCIH1650_LUNG
0.143886772763713
NCIH1693_LUNG
0.124858004409791
NCIH1703_LUNG
0.122857753065183
NCIH1792_LUNG
0.130016488732899
NCIH1944_LUNG
0.117469879237864
NCIH2023_LUNG
0.1351739855138
NCIH2030_LUNG
0.126989230159551
NCIH2087_LUNG
0.130160035769625
NCIH2122_LUNG
0.121636385413356
NCIH2170_LUNG
0.12365955316306
NCIH2172_LUNG
0.128340693576546
NCIH23_LUNG
0.125817370868064
NCIH322_LUNG
0.127670908043556
NCIH441_LUNG
0.132165281974901
NCIH460_LUNG
0.109316837775914
NCIH520_LUNG
0.0978809119795495
PC14_LUNG
0.13793056567663
RERFLCAI_LUNG
0.127522769035006
SKMES1_LUNG
0.134541775398636
In [275]:
select_cl
A data.table: 93 × 27
cell_linen_replicatesclean_cell_line_namecell_line_SSMDSSMD_failureculture_typeculture_mediumculture_codealiasesprimary_tissue⋯TargetDoses (uM)Activity Data (median)Activity SDNum DataFitTypeEC50 (uM)IC50 (uM)AmaxActArea
<chr><int><chr><dbl><lgl><chr><chr><chr><chr><chr>⋯<chr><chr><chr><chr><int><chr><dbl><dbl><dbl><dbl>
A549_LUNG 4A549 -1.939855FALSEAdherent DMEM; 10% FBS DMEM001A549 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,84.41,-.17,-9.7,-12,-5.8,-25,-29,-77 11.7,7.54,13.8,5.52,20.7,2.17,8.75,6.108Sigmoid 8.692716264.22724628-75.15564730.6707
A549_LUNG 4A549 -1.939855FALSEAdherent DMEM; 10% FBS DMEM001A549 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-16,-7.4,-10,-7.1,-9.5,-12,-27,-37 2.25,2.07,6.74,1.32,1.54,.97,3.71,3.82 8Sigmoid 2.060424098.00000000-36.77396770.4353
A549_LUNG 4A549 -1.939855FALSEAdherent DMEM; 10% FBS DMEM001A549 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-3.6,-1.1,4.70,-.63,3.12,-11,-25,-43 .61,1.43,5.41,.84,3.71,0,4.15,6.68 8Sigmoid 2.394717698.00000000-43.08367540.4884
CALU6_LUNG 2CALU6 -1.061810FALSE EMEM: 90.0% 10%FBS Calu-6 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-17,-2.6,8.33,-19,5.39,-2.4,-10,-20 27.9,5.25,9.48,17.0,6.09,2.24,6.38,6.088Constant NA8.00000000 -4.83551030.1923
CALU6_LUNG 2CALU6 -1.061810FALSE EMEM: 90.0% 10%FBS Calu-6 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-7.4,-19,-13,7.88,.92,-7.6,-7.3,-18 1.28,4.50,26.7,36.8,.43,13.8,2.84,11.2 8Constant NA8.00000000 -8.69999980.7187
CALU6_LUNG 2CALU6 -1.061810FALSE EMEM: 90.0% 10%FBS Calu-6 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,815.5,-2.8,18.9,16.3,15.8,-4.6,3.92,-5311.8,8.28,9.89,6.67,8.08,7.41,6.10,3.588Sigmoid 8.704103257.80798435-51.73450470.4963
HARA_LUNG 2HARA -1.372030FALSE RPMI; 10% FBS HARA lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,83.54,3.77,5.40,-3.6,-5.6,-7.5,-27,-56 24.1,9.97,8.00,3.19,4.96,4.41,.95,2.06 8Sigmoid 8.861800426.65674067-56.25913620.7284
HARA_LUNG 2HARA -1.372030FALSE RPMI; 10% FBS HARA lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-16,-20,-19,-13,.50,-1.9,-35,-46 12.7,4.09,19.3,7.30,9.93,4.63,2.85,5.758Sigmoid 2.151573428.00000000-45.91326901.1713
HARA_LUNG 2HARA -1.372030FALSE RPMI; 10% FBS HARA lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,84.60,-.54,-1.5,.12,-6.1,-14,-42,-44 13.4,10.6,8.83,1.36,18.6,14.6,.92,.43 8Sigmoid 1.001384748.00000000-44.40664670.8434
HCC15_LUNG 2HCC15 -2.516822FALSE RPMI; 10% FBS HCC15 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-1.8,-2.8,-7.8,6.21,-9.0,-4.3,-11,-8.124.5,8.60,14.8,8.00,14.0,13.3,22.0,3.798Constant NA8.00000000 -2.61007000.6883
HCC15_LUNG 2HCC15 -2.516822FALSE RPMI; 10% FBS HCC15 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,85.44,-2.3,6.74,-.19,10.5,-3.1,-16,-30 .14,3.17,3.55,9.27,.41,7.36,9.64,2.32 8Sigmoid 2.197055828.00000000-30.32488060.9135
HCC15_LUNG 2HCC15 -2.516822FALSE RPMI; 10% FBS HCC15 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-3.0,.70,1.30,-7.1,-1.1,-7.3,1.14,-7.020.6,6.42,20.1,17.3,18.1,4.82,7.12,3.088Constant NA8.00000000 -3.86520620.3449
HCC827_LUNG 3HCC827 -2.097436FALSEadherentepithelialdensity >30%RPMI; 10% FBS RPMI001HCC827 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-1.5,23.7,-12,23.9,-4.4,.62,-19,-81 12.3,5.57,9.59,9.98,17.1,8.29,29.2,2.668Linear NA0.37213399-81.47216800.9560
HCC827_LUNG 3HCC827 -2.097436FALSEadherentepithelialdensity >30%RPMI; 10% FBS RPMI001HCC827 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-18,4.02,-21,8.25,-44,-75,-89,-89 7.20,17.6,21.9,17.3,4.25,4.89,3.19,1.478Sigmoid 0.307156300.31740648-88.99983223.1181
HCC827_LUNG 3HCC827 -2.097436FALSEadherentepithelialdensity >30%RPMI; 10% FBS RPMI001HCC827 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,84.85,1.47,-33,-72,-83,-80,-86,-90 26.3,14.2,14.2,5.34,.98,6.57,9.09,.13 8Sigmoid 0.029334970.03891792-85.47996524.4132
KNS62_LUNG 2KNS62 -2.008283FALSE MEMalpha; 10% FBS KNS-62 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,84.89,12.2,13.3,8.62,22.9,1.10,-7.1,-3119.6,19.8,1.31,18.3,15.3,11.9,14.6,23.48Sigmoid 3.775160078.00000000-30.05010220.2334
KNS62_LUNG 2KNS62 -2.008283FALSE MEMalpha; 10% FBS KNS-62 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-19,-28,-18,-17,15.4,-13,-16,-11 14.7,32.0,3.36,22.8,26.9,16.6,9.39,2.298Constant NA8.00000000-10.89843271.0744
KNS62_LUNG 2KNS62 -2.008283FALSE MEMalpha; 10% FBS KNS-62 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,88.28,20.5,-2.4,5.25,-7.9,-21,-1.6,23.114.6,2.50,33.5,8.66,1.92,2.23,.74,34.6 8Constant NA8.00000000 -0.80612470.1777
LCLC103H_LUNG2LCLC103H-1.292554FALSE RPMI;10% FBS; LCLC-103Hlung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8.84,4.48,.80,1.30,-.87,-2.8,3.90,-12 17.5,3.10,1.19,1.22,15.7,8.88,1.05,1.268Constant NA8.00000000 1.85609400.1241
LCLC103H_LUNG2LCLC103H-1.292554FALSE RPMI;10% FBS; LCLC-103Hlung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-20,-16,-15,-22,-11,-16,-18,-13 10.6,4.63,12.8,3.42,10.4,4.44,8.28,8.238Constant NA8.00000000-18.33722691.4051
LCLC103H_LUNG2LCLC103H-1.292554FALSE RPMI;10% FBS; LCLC-103Hlung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,81.09,-3.8,1.02,3.71,1.54,4.05,-5.1,-154.09,11.2,11.3,15.6,.85,23.1,11.4,7.10 8Constant NA8.00000000 -0.33088650.2600
LUDLU1_LUNG 2LUDLU1 -1.201608FALSEAdherent RPMI; 10% FBS LUDLU-1 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,811.8,12.6,-17,-8.8,-30,-34,-40,-61 6.41,17.3,22.2,12.0,16.2,23.5,26.6,16.18Linear NA2.31964210-61.47478481.8359
LUDLU1_LUNG 2LUDLU1 -1.201608FALSEAdherent RPMI; 10% FBS LUDLU-1 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-6.6,-9.9,-1.7,-21,2.63,-35,-50,-67 11.1,17.4,5.29,17.7,10.3,9.18,12.2,8.808Sigmoid 1.016152981.82598734-64.45858761.5094
LUDLU1_LUNG 2LUDLU1 -1.201608FALSEAdherent RPMI; 10% FBS LUDLU-1 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,814.0,5.44,2.74,-13,-10,-48,-52,-64 18.7,48.1,13.4,9.06,14.2,3.47,9.53,3.348Sigmoid 0.336647271.65249217-63.64346691.5519
MORCPR_LUNG 3MORCPR -1.926500FALSE MOR/CPR lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,814.3,3.18,13.4,-.22,13.3,-5.9,-20,-26 5.66,1.54,7.53,2.11,14.8,3.87,13.9,.41 8Sigmoid 1.098604328.00000000-26.05660440.3634
MORCPR_LUNG 3MORCPR -1.926500FALSE MOR/CPR lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-27,-8.5,4.08,-12,-17,6.88,-10,-27 12.0,16.0,4.67,.38,21.4,6.52,5.94,1.47 8Constant NA8.00000000-12.33875850.7898
MORCPR_LUNG 3MORCPR -1.926500FALSE MOR/CPR lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,84.61,-13,2.33,2.81,-12,-7.1,-4.8,-17 2.42,21.5,.53,12.7,6.18,4.69,18.2,5.87 8Constant NA8.00000000 -4.80092000.2041
NCIH1299_LUNG2NCIH1299-1.197529FALSEAdherent RPMI; 10% FBS NCI-H1299lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,87.14,22.3,16.5,5.65,8.67,.16,-19,-9.0 2.10,14.6,1.45,21.6,5.31,13.2,27.8,13.88Sigmoid 0.616384338.00000000 -9.64271930.2706
NCIH1299_LUNG2NCIH1299-1.197529FALSEAdherent RPMI; 10% FBS NCI-H1299lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-1.2,7.69,-18,-7.6,-.17,-15,-11,-26 11.2,9.86,18.8,7.36,17.6,10.3,11.2,8.848Constant NA8.00000000 -8.26504900.7984
NCIH1299_LUNG2NCIH1299-1.197529FALSEAdherent RPMI; 10% FBS NCI-H1299lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,816.9,9.94,9.77,-3.0,4.30,-12,-15,-43 13.0,.64,.78,3.41,2.87,2.38,9.43,.10 8Sigmoid 8.627411378.00000000-40.98887630.7121
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋱⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
NCIH2170_LUNG3NCIH2170-1.204793FALSE RPMI;10% FBS; NCI-H2170 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8.11,14.4,-15,-18,-43,-74,-83,-86 10.0,14.5,5.16,17.9,6.69,14.4,8.60,14.68Sigmoid 0.228014380.30098090-88.11788183.14510
NCIH2170_LUNG3NCIH2170-1.204793FALSE RPMI;10% FBS; NCI-H2170 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-4.6,-23,6.94,-.55,-20,-22,-50,-73 5.78,15.4,2.42,13.5,7.68,8.98,8.04,.93 8Sigmoid 2.740792512.65564489-72.94126131.89220
NCIH2170_LUNG3NCIH2170-1.204793FALSE RPMI;10% FBS; NCI-H2170 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,87.61,-.16,27.6,12.1,-14,-28,-60,-84 11.0,8.38,15.7,9.31,7.45,29.0,10.8,8.378Sigmoid 1.180775641.56435514-82.49170682.03070
NCIH2172_LUNG2NCIH2172-1.750465FALSE RPMI-1640: 90.0% NCI-H2172 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-2.2,-12,-1.4,5.69,12.9,1.65,12.2,-23 6.86,19.6,11.3,15.5,24.5,27.7,12.0,35.68Constant NA8.00000000 0.99034330.03765
NCIH2172_LUNG2NCIH2172-1.750465FALSE RPMI-1640: 90.0% NCI-H2172 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-74,-13,-21,1.99,-11,18.4,-49,-9.5 18.3,32.2,13.6,19.7,24.3,18.5,15.7,23.18Constant NA8.00000000-18.44103050.44160
NCIH2172_LUNG2NCIH2172-1.750465FALSE RPMI-1640: 90.0% NCI-H2172 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,832.7,-21,18.1,-39,12.0,-33,13.4,-36 60.6,29.2,54.4,11.1,66.9,19.3,22.7,9.338Constant NA8.00000000-19.36130710.19800
NCIH23_LUNG 4NCIH23 -2.615303FALSEAdherentRPMI; 10% FBS RPMI001 NCI-H23 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,87.87,17.0,-.32,16.7,-.47,-3.2,8.20,-41 6.16,5.64,11.9,1.47,1.32,5.46,8.32,9.838Sigmoid 8.230145358.00000000-40.86384960.43020
NCIH23_LUNG 4NCIH23 -2.615303FALSEAdherentRPMI; 10% FBS RPMI001 NCI-H23 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-38,-47,-26,-17,9.44,4.88,-8.0,-1.7 19.3,58.9,22.7,9.80,20.5,8.29,14.9,.11 8Linear NA8.00000000-44.16484831.20230
NCIH23_LUNG 4NCIH23 -2.615303FALSEAdherentRPMI; 10% FBS RPMI001 NCI-H23 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,87.09,15.0,8.97,11.3,7.07,-4.1,-3.2,-3.7.72,9.28,11.9,8.86,2.28,2.57,3.73,10.1 8Constant NA8.00000000 5.46319770.00000
NCIH322_LUNG 4NCIH322 -1.244356FALSEAdherentRPMI;10% FBS; 2%GlutamineMCCOYS5A001NCI-H322 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,86.34,7.33,-3.3,.21,-14,-24,-76,-91 4.67,6.12,5.50,.51,11.6,1.02,1.01,1.91 8Sigmoid 1.252795341.36212850-91.16526791.34630
NCIH322_LUNG 4NCIH322 -1.244356FALSEAdherentRPMI;10% FBS; 2%GlutamineMCCOYS5A001NCI-H322 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-20,-11,-12,-9.6,1.63,-16,-72,-82 22.1,7.09,3.41,12.7,1.74,1.50,.029,.83 8Sigmoid 1.484435561.60569716-81.65052031.46520
NCIH322_LUNG 4NCIH322 -1.244356FALSEAdherentRPMI;10% FBS; 2%GlutamineMCCOYS5A001NCI-H322 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,86.24,2.04,.46,-6.7,-22,-66,-76,-86 5.78,3.09,4.89,6.13,2.34,9.81,.23,6.07 8Sigmoid 0.483234790.64464504-84.20258331.97460
NCIH441_LUNG 4NCIH441 -2.228915FALSEAdherentRPMI; 10% FBS RPMI001 NCI-H441 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8.68,2.12,8.83,-2.4,1.78,-21,-3.3,-33 4.66,3.48,25.1,2.82,20.7,7.53,10.3,6.008Sigmoid 8.568929628.00000000-31.09734150.43460
NCIH441_LUNG 4NCIH441 -2.228915FALSEAdherentRPMI; 10% FBS RPMI001 NCI-H441 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-11,-5.2,-15,-5.2,-4.7,-9.4,-12,-40 8.07,12.9,17.4,3.33,5.80,1.64,7.61,8.548Sigmoid 6.271634588.00000000-40.12529370.82770
NCIH441_LUNG 4NCIH441 -2.228915FALSEAdherentRPMI; 10% FBS RPMI001 NCI-H441 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,814.5,1.35,20.6,9.63,13.5,-.46,-10,-29 17.7,.43,7.34,5.11,8.21,1.76,6.97,1.92 8Sigmoid 3.365048898.00000000-28.69470220.30660
NCIH460_LUNG 2NCIH460 -1.737824FALSEAdherentRPMI; 10% FBS NCI-H460 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,83.32,12.7,2.62,6.75,-6.1,-4.4,-2.3,-2.010.6,15.7,11.2,1.23,10.3,14.8,3.69,6.718Constant NA8.00000000 1.47314890.11020
NCIH460_LUNG 2NCIH460 -1.737824FALSEAdherentRPMI; 10% FBS NCI-H460 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-15,-3.6,1.54,-21,1.19,-.71,-25,-20 1.44,10.4,1.29,5.08,3.95,1.83,2.05,4.018Constant NA8.00000000-12.51180550.87230
NCIH460_LUNG 2NCIH460 -1.737824FALSEAdherentRPMI; 10% FBS NCI-H460 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8.052,.18,8.84,.73,7.74,-5.0,-2.6,-6.4 3.51,1.25,4.97,7.09,2.83,10.8,3.32,3.018Constant NA8.00000000 0.28856490.06994
NCIH520_LUNG 3NCIH520 -1.536423FALSE RPMI; 10% FBS NCI-H520 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,84.37,7.12,3.38,6.10,1.41,-.95,1.57,-.6811.4,18.3,19.2,15.4,3.21,10.7,14.3,7.158Constant NA8.00000000 1.41284800.00000
NCIH520_LUNG 3NCIH520 -1.536423FALSE RPMI; 10% FBS NCI-H520 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-12,-14,-4.5,-18,-2.2,-20,-11,-39 2.72,9.40,11.0,6.70,3.67,3.07,5.25,11.18Sigmoid 8.207516258.00000000-38.67092510.83200
NCIH520_LUNG 3NCIH520 -1.536423FALSE RPMI; 10% FBS NCI-H520 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-1.1,.58,2.58,4.28,2.13,12.9,3.81,-1.1 5.27,5.64,14.2,11.1,13.6,6.58,24.7,.26 8Constant NA8.00000000 0.73898260.00000
PC14_LUNG 3PC14 -1.521806FALSE RPMI; 10% FBS PC-14 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,84.39,1.92,6.76,1.95,-.43,-25,-54,-97 3.51,.93,5.27,4.91,3.41,5.81,6.96,.94 8Sigmoid 3.442031622.14965892-96.62354281.62710
PC14_LUNG 3PC14 -1.521806FALSE RPMI; 10% FBS PC-14 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-22,-13,-12,-22,-55,-75,-82,-82 7.20,4.07,3.15,.31,2.29,1.49,1.00,3.83 8Sigmoid 0.212715160.22603795-81.47634893.32910
PC14_LUNG 3PC14 -1.521806FALSE RPMI; 10% FBS PC-14 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8.95,-17,-31,-60,-67,-75,-78,-91 1.57,2.76,2.27,2.23,1.32,.39,.83,.91 8Sigmoid 0.013407790.06461856-86.95714573.71770
RERFLCAI_LUNG2RERFLCAI-1.207378FALSEAdherentEMEM: 10% FBS RERF-LC-AIlung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-3.2,-2.6,-5.4,-2.3,-3.1,-4.2,-12,-25 4.57,5.48,4.03,4.92,5.02,2.21,1.62,.43 8Sigmoid 3.192855368.00000000-25.10490420.30600
RERFLCAI_LUNG2RERFLCAI-1.207378FALSEAdherentEMEM: 10% FBS RERF-LC-AIlung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-5.6,-8.5,-1.9,-2.5,.57,-2.3,-9.4,-19 1.70,2.28,.36,5.16,9.63,2.31,1.76,3.00 8Constant NA8.00000000 -5.89391570.49540
RERFLCAI_LUNG2RERFLCAI-1.207378FALSEAdherentEMEM: 10% FBS RERF-LC-AIlung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-1.9,1.68,-6.0,-1.3,-1.1,-2.5,-11,-27 2.31,2.84,6.22,3.84,6.82,4.58,4.34,5.008Sigmoid 3.556686888.00000000-26.91756820.29990
SKMES1_LUNG 2SKMES1 -1.317730FALSEAdherentDMEM; 10% FBS SK-MES-1 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,822.4,13.9,27.1,9.43,-11,-37,-61,-92 .99,10.8,11.2,11.1,1.28,10.8,6.88,2.16 8Sigmoid 1.548179391.47644412-90.78666692.02590
SKMES1_LUNG 2SKMES1 -1.317730FALSEAdherentDMEM; 10% FBS SK-MES-1 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,8-10,9.20,-19,-17,-5.9,-39,-64,-94 13.3,15.5,.26,11.2,24.1,11.1,2.83,.97 8Sigmoid 5.732225421.42946458-93.82552342.08880
SKMES1_LUNG 2SKMES1 -1.317730FALSEAdherentDMEM; 10% FBS SK-MES-1 lung_NSC⋯EGFR.0025,.0080,.025,.080,.25,.80,2.53,818.0,17.2,3.24,-14,-21,-42,-74,-90 11.4,8.40,18.9,17.9,5.17,13.4,2.34,.0778Sigmoid 1.085312370.96515131-91.42735292.37910
In [276]:
drug_res <- select_cl %>% 
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`)) %>% filter(cell_line %in% names(score))
head(drug_res)
A data.table: 6 × 3
cell_lineIC50 (uM)EC50 (uM)
<chr><dbl><dbl>
A549_LUNG 4.2272468.692716
A549_LUNG 8.0000002.060424
A549_LUNG 8.0000002.394718
CALU6_LUNG8.000000 NA
CALU6_LUNG8.000000 NA
CALU6_LUNG7.8079848.704103
In [277]:
drug_res$score = score[as.character(drug_res$cell_line)]
In [278]:
#cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method =  'pearson')
#cor.test(drug_res$LN_IC50,drug_res$score,method =  'kendall')
cor.test(drug_res$`IC50 (uM)`,drug_res$score,alternative = 'less',method =  'spearman')
cor.test(drug_res$`EC50 (uM)`,drug_res$score,alternative = 'less',method =  'spearman')
Warning message in cor.test.default(drug_res$`IC50 (uM)`, drug_res$score, alternative = "less", :
“Cannot compute exact p-value with ties”
	Spearman's rank correlation rho

data:  drug_res$`IC50 (uM)` and drug_res$score
S = 180450, p-value = 0.0003379
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.3462008 
Warning message in cor.test.default(drug_res$`EC50 (uM)`, drug_res$score, alternative = "less", :
“Cannot compute exact p-value with ties”
	Spearman's rank correlation rho

data:  drug_res$`EC50 (uM)` and drug_res$score
S = 44641, p-value = 0.08214
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.1803435 
In [185]:
score <- apply(score_df[,2:32],MARGIN  = 2,FUN = FUN5)
In [186]:
drug_res$score = score[as.character(drug_res$cell_line)]
In [187]:
#cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method =  'pearson')
#cor.test(drug_res$LN_IC50,drug_res$score,method =  'kendall')
cor.test(drug_res$`IC50 (uM)`,drug_res$score,alternative = 'less',method =  'spearman')
cor.test(drug_res$`EC50 (uM)`,drug_res$score,alternative = 'less',method =  'spearman')
Warning message in cor.test.default(drug_res$`IC50 (uM)`, drug_res$score, alternative = "less", :
“Cannot compute exact p-value with ties”
	Spearman's rank correlation rho

data:  drug_res$`IC50 (uM)` and drug_res$score
S = 156121, p-value = 0.05733
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.1646965 
Warning message in cor.test.default(drug_res$`EC50 (uM)`, drug_res$score, alternative = "less", :
“Cannot compute exact p-value with ties”
	Spearman's rank correlation rho

data:  drug_res$`EC50 (uM)` and drug_res$score
S = 37114, p-value = 0.5567
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
0.01865623 
In [ ]:

In [191]:
score_df <- merge(EGFR_drug_exp,drivers_pos,by = 'V1',all = F)
score <- apply(score_df[,2:32],MARGIN  = 2,FUN = FUN1)

drug_res <- select_cl %>% 
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`)) %>% filter(cell_line %in% names(score))
drug_res$score = score[as.character(drug_res$cell_line)]
In [192]:
cor.test(drug_res$`IC50 (uM)`,drug_res$score,alternative = 'less',method =  'spearman')
cor.test(drug_res$`EC50 (uM)`,drug_res$score,alternative = 'less',method =  'spearman')
Warning message in cor.test.default(drug_res$`IC50 (uM)`, drug_res$score, alternative = "less", :
“Cannot compute exact p-value with ties”
	Spearman's rank correlation rho

data:  drug_res$`IC50 (uM)` and drug_res$score
S = 183277, p-value = 0.0001464
alternative hypothesis: true rho is less than 0
sample estimates:
     rho 
-0.36729 
Warning message in cor.test.default(drug_res$`EC50 (uM)`, drug_res$score, alternative = "less", :
“Cannot compute exact p-value with ties”
	Spearman's rank correlation rho

data:  drug_res$`EC50 (uM)` and drug_res$score
S = 42137, p-value = 0.1906
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.1141338 
In [193]:
score_df <- merge(EGFR_drug_exp,drivers_neg,by = 'V1',all = F)
score <- apply(score_df[,2:32],MARGIN  = 2,FUN = FUN1)

drug_res <- select_cl %>% 
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`)) %>% filter(cell_line %in% names(score))
drug_res$score = score[as.character(drug_res$cell_line)]
In [194]:
cor.test(drug_res$`IC50 (uM)`,drug_res$score,alternative = 'less',method =  'spearman')
cor.test(drug_res$`EC50 (uM)`,drug_res$score,alternative = 'less',method =  'spearman')
Warning message in cor.test.default(drug_res$`IC50 (uM)`, drug_res$score, alternative = "less", :
“Cannot compute exact p-value with ties”
	Spearman's rank correlation rho

data:  drug_res$`IC50 (uM)` and drug_res$score
S = 178481, p-value = 0.0005852
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.3315071 
Warning message in cor.test.default(drug_res$`EC50 (uM)`, drug_res$score, alternative = "less", :
“Cannot compute exact p-value with ties”
	Spearman's rank correlation rho

data:  drug_res$`EC50 (uM)` and drug_res$score
S = 48171, p-value = 0.0164
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.2737041 

pipe¶

read data¶

In [203]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_sen_res'
#GDSC
test_drug <- 'Osimertinib'
cancer_type <- 'LUAD'
#CCLE
CCLE_Target <- 'EGFR'
CCLE_tumor_type <- 'Lung'
#gene <- 'SLC1A5'

read_dir <- file.path(read_dir,run_name)
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))

drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))

drivers$directions = drivers$weight_grad_total_dir_mean >= 0
drivers$directions_cal = as.numeric(drivers$directions)
drivers[!drivers$directions,]$directions_cal = -1
In [204]:
GDSC_exp <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/Cell_line_RMA_proc_basalExp.txt')
GDSC_compounds <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/screened_compounds_rel_8.5.csv')
GDSC_cellline <- openxlsx::read.xlsx('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/Cell_Lines_Details.xlsx')
GDSC_drug1 <- openxlsx::read.xlsx('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/GDSC1_fitted_dose_response_27Oct23.xlsx')
GDSC_drug2 <- openxlsx::read.xlsx('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/GDSC2_fitted_dose_response_27Oct23.xlsx')
GDSC_drug <- rbind(GDSC_drug1,GDSC_drug2)
In [205]:
GDSC_select <- GDSC_drug %>% dplyr::filter(GDSC_drug$COSMIC_ID %in% substring(colnames(GDSC_exp),6)) %>%
    dplyr::filter(DRUG_NAME == test_drug) %>%
    dplyr::filter(TCGA_DESC == cancer_type) 

#GDSC_select
In [206]:
GDSC_select_exp <- GDSC_exp %>% dplyr::select(c(1,which(substring(colnames(GDSC_exp),6) %in% GDSC_select$COSMIC_ID)))
In [207]:
CCLE_exp <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/CCLE_DepMap_18Q1_RNAseq_RPKM_20180214.gct')
CCLE_info <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/sample_info.csv')
CCLE_drug <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/CCLE_NP24.2009_Drug_data_2015.02.24.csv')
CCLE_drug_info <- readxl::read_excel('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/CCLE_GNF_data_090613.xls')
In [208]:
#利用aggregate函数,对相同的基因名按列取平均
#CCLE_exp <- aggregate(.~Description,mean,data=CCLE_exp)

CCLE_merge_info <- merge(CCLE_info,CCLE_drug,by.x = 'cell_line',by.y='CCLE Cell Line Name',all = F)

CCLE_select <- CCLE_merge_info %>% 
    filter(Target == CCLE_Target) %>% 
    filter(tumor_type == CCLE_tumor_type)

CCLE_select_name <-  CCLE_select%>%
    dplyr::select('cell_line') %>% 
    t() %>% c()

CCLE_drug_exp <- CCLE_exp %>% 
    dplyr::select(c('Description',CCLE_select_name)) %>%
    dplyr::filter(Description %in% drivers$V1) %>% 
    as.data.frame()

test single gene¶

GDSC¶

In [1004]:
pvalue_list <- c()
corr_list <- c()
gene_list <- drivers$V1[drivers$V1 %in% GDSC_exp$GENE_SYMBOLS]
for ( gene in gene_list){
    message(gene)
    GDSC_single_gene <-  GDSC_select_exp%>% dplyr::filter(GENE_SYMBOLS == gene) %>% t() %>% as.data.frame()
    GDSC_single_gene <- GDSC_single_gene %>% mutate(COSMIC_ID = substring(rownames(GDSC_single_gene),6))
    GDSC_single_gene <- GDSC_single_gene[2:nrow(GDSC_single_gene),] %>% apply(2,as.numeric)
    colnames(GDSC_single_gene) <- c('gene','COSMIC_ID') 
    GDSC_single_gene <- GDSC_single_gene %>% as.data.frame()
    GDSC_single_gene$group <- GDSC_single_gene$gene >= mean(GDSC_single_gene$gene) %>% as.numeric()

    cor_df <- GDSC_select %>% 
    dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% 
    merge(GDSC_single_gene,by = 'COSMIC_ID',all = T)


    pvalue_list <- cor.test(cor_df$LN_IC50,cor_df$gene,alternative = 'two.sided',method =  'pearson')[[3]] %>% c(pvalue_list,.)
    corr_list <- cor.test(cor_df$LN_IC50,cor_df$gene,alternative = 'two.sided',method =  'pearson')[[4]] %>% c(corr_list,.)
}

gene_cor_plot_df <- data.frame(gene = gene_list,pvalue= pvalue_list,corr = corr_list)
GTF2B

MSH6

TFDP1

MYO1B

TUBA1B

SLC1A5

TGM2

BARD1

TUBB4B

RRBP1

ANXA2

PLK2

PSMC4

DGKE

CEBPD

HSPA2

RAB31

TFAP2C

CD9

TPM1

LMO7

KLF5

TRIB1

HIST1H1C

DAAM1

In [1005]:
pvalue_list <- c()
corr_list <- c()
gene_list <- drivers$V1[drivers$V1 %in% GDSC_exp$GENE_SYMBOLS]
for ( gene in gene_list){
    message(gene)
    GDSC_single_gene <-  GDSC_select_exp%>% dplyr::filter(GENE_SYMBOLS == gene) %>% t() %>% as.data.frame()
    GDSC_single_gene <- GDSC_single_gene %>% mutate(COSMIC_ID = substring(rownames(GDSC_single_gene),6))
    GDSC_single_gene <- GDSC_single_gene[2:nrow(GDSC_single_gene),] %>% apply(2,as.numeric)
    colnames(GDSC_single_gene) <- c('gene','COSMIC_ID') 
    GDSC_single_gene <- GDSC_single_gene %>% as.data.frame()
    GDSC_single_gene$group <- GDSC_single_gene$gene >= mean(GDSC_single_gene$gene) %>% as.numeric()

    cor_df <- GDSC_select %>% 
    dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% 
    merge(GDSC_single_gene,by = 'COSMIC_ID',all = T)


    pvalue_list <- cor.test(cor_df$AUC,cor_df$gene,alternative = 'two.sided',method =  'pearson')[[3]] %>% c(pvalue_list,.)
    corr_list <- cor.test(cor_df$AUC,cor_df$gene,alternative = 'two.sided',method =  'pearson')[[4]] %>% c(corr_list,.)
}

gene_cor_plot_df_auc <- data.frame(gene = gene_list,pvalue= pvalue_list,corr = corr_list)
GTF2B

MSH6

TFDP1

MYO1B

TUBA1B

SLC1A5

TGM2

BARD1

TUBB4B

RRBP1

ANXA2

PLK2

PSMC4

DGKE

CEBPD

HSPA2

RAB31

TFAP2C

CD9

TPM1

LMO7

KLF5

TRIB1

HIST1H1C

DAAM1

In [1006]:
gene_cor_plot_df$with = 'IC50'
gene_cor_plot_df_auc$with = 'AUC'
gene_cor_plot_df <- rbind(gene_cor_plot_df,gene_cor_plot_df_auc)
In [1007]:
gene_cor_plot_df$p_status <- gene_cor_plot_df$pvalue <= 0.05
In [1008]:
gene_cor_plot_df
A data.frame: 50 × 5
genepvaluecorrwithp_status
<chr><dbl><dbl><chr><lgl>
GTF2B 9.714737e-01 0.004675303IC50FALSE
MSH6 7.513098e-01-0.041415908IC50FALSE
TFDP1 2.885206e-01 0.138099376IC50FALSE
MYO1B 1.299438e-02-0.316363859IC50 TRUE
TUBA1B 2.074346e-01 0.163705070IC50FALSE
SLC1A5 4.410045e-01 0.100480841IC50FALSE
TGM2 9.086580e-01-0.014999345IC50FALSE
BARD1 4.543944e-01-0.097575263IC50FALSE
TUBB4B 5.366215e-01-0.080657379IC50FALSE
RRBP1 2.317320e-01-0.155403122IC50FALSE
ANXA2 9.750867e-01 0.004082954IC50FALSE
PLK2 4.505011e-01-0.098415166IC50FALSE
PSMC4 1.889890e-01 0.170478791IC50FALSE
DGKE 3.739904e-01 0.115840427IC50FALSE
CEBPD 4.153328e-01-0.106193334IC50FALSE
HSPA2 9.242777e-01-0.012426096IC50FALSE
RAB31 6.757715e-01-0.054640531IC50FALSE
TFAP2C 2.013561e-01-0.165888109IC50FALSE
CD9 4.987845e-01-0.088261276IC50FALSE
TPM1 2.873641e-01 0.138427871IC50FALSE
LMO7 4.635234e-01 0.095621202IC50FALSE
KLF5 1.557443e-02-0.308475948IC50 TRUE
TRIB1 9.899296e-01 0.001650178IC50FALSE
HIST1H1C4.762866e-01 0.092923887IC50FALSE
DAAM1 1.843256e-05-0.518766520IC50 TRUE
GTF2B 9.624117e-01 0.006161451AUC FALSE
MSH6 6.555349e-01-0.058272459AUC FALSE
TFDP1 2.715547e-01 0.143008635AUC FALSE
MYO1B 4.529751e-02-0.257304347AUC TRUE
TUBA1B 1.858683e-01 0.171671061AUC FALSE
SLC1A5 5.043171e-01 0.087131579AUC FALSE
TGM2 6.776099e-01-0.054312656AUC FALSE
BARD1 2.644980e-01-0.145110732AUC FALSE
TUBB4B 4.437621e-01-0.099878491AUC FALSE
RRBP1 1.407588e-01-0.190804506AUC FALSE
ANXA2 7.805462e-01 0.036413499AUC FALSE
PLK2 4.847022e-01-0.091166469AUC FALSE
PSMC4 4.423388e-02 0.258544093AUC TRUE
DGKE 5.223808e-01 0.083486580AUC FALSE
CEBPD 5.670318e-01-0.074735438AUC FALSE
HSPA2 4.755087e-01 0.093087162AUC FALSE
RAB31 6.615541e-01-0.057187687AUC FALSE
TFAP2C 3.154220e-01-0.130688857AUC FALSE
CD9 2.719604e-01-0.142888923AUC FALSE
TPM1 1.554080e-01 0.184150134AUC FALSE
LMO7 5.285555e-01 0.082255227AUC FALSE
KLF5 1.940409e-01-0.168578308AUC FALSE
TRIB1 9.197905e-01 0.013164815AUC FALSE
HIST1H1C2.410690e-01 0.152373946AUC FALSE
DAAM1 1.178342e-03-0.405733131AUC TRUE
In [961]:
p1 <- ggplot(data = gene_cor_plot_df)+
    geom_point(aes(x = with,y = gene,size=abs(corr),color = p_status))+
    geom_text(aes(x = with,y = gene,label = round(corr,2)),color = 'white')+
    scale_size_continuous(range = c(8, 15))+
    scale_colour_manual(values = c('TRUE'= "#8C0303", 'FALSE' = "#2A398C"))+
    theme_bw()
p1
No description has been provided for this image
In [109]:
ggsave(p1,filename = paste0(run_name,'_',test_drug,'_',cancer_type,'_genecor_with_drug.pdf'),height = 10,width =5)

CCLE¶

In [962]:
rn <- CCLE_drug_exp$Description
CCLE_drug_exp <- CCLE_drug_exp[,2:ncol(CCLE_drug_exp)]
rownames(CCLE_drug_exp) <- rn
CCLE_drug_exp <- log2(CCLE_drug_exp)
CCLE_drug_exp$GENE_SYMBOLS = rownames(CCLE_drug_exp)
In [963]:
table(CCLE_select$Compound)
Erlotinib Lapatinib   ZD-6474 
       31        31        31 
In [964]:
CCLE_surv <-  CCLE_select %>% filter(Compound == 'Erlotinib') %>%
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`))
In [965]:
gene = 'GTF2B'
cor_df <- CCLE_drug_exp %>% dplyr::filter(GENE_SYMBOLS == gene) %>% t() %>% as.data.frame()%>% 
mutate(cell_line = colnames(CCLE_drug_exp)) %>%
mutate(exp = as.numeric(CCLE_drug_exp[gene,])) %>%
dplyr::select(c('exp','cell_line')) %>%
merge(y =CCLE_surv,by = 'cell_line',all = F)
#GDSC_single_gene <- GDSC_single_gene[2:nrow(GDSC_single_gene),] %>% apply(2,as.numeric)
#colnames(GDSC_single_gene) <- c('gene','COSMIC_ID') 
#GDSC_single_gene <- GDSC_single_gene %>% as.data.frame()
pvalue_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method =  'pearson')[[3]] 
corr_list <- cor.test(cor_df$`EC50 (uM)`,cor_df$exp,alternative = 'two.sided',method =  'pearson')[[4]] 
pvalue_list
corr_list
Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
0.929754179624599
cor: -0.0903369786114959
In [966]:
pvalue_list <- c()
corr_list <- c()
gene_list <- drivers$V1[drivers$V1 %in% CCLE_drug_exp$GENE_SYMBOLS]

CCLE_surv <-  CCLE_select %>% filter(Compound == 'Erlotinib') %>%
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`))

for ( gene in gene_list){
    message(gene)
    cor_df <- CCLE_drug_exp %>% dplyr::filter(GENE_SYMBOLS == gene) %>% t() %>% as.data.frame()%>% 
                mutate(cell_line = colnames(CCLE_drug_exp)) %>%
                mutate(exp = as.numeric(CCLE_drug_exp[gene,])) %>%
                dplyr::select(c('exp','cell_line')) %>%
                merge(y =CCLE_surv,by = 'cell_line',all = F)


    pvalue_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method =  'pearson')[[3]] %>% c(pvalue_list,.)
    corr_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method =  'pearson')[[4]] %>% c(corr_list,.)
}

cor_plot_df_ic50 <- data.frame(gene = gene_list,pvalue= pvalue_list,corr = corr_list)
GTF2B

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
MSH6

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TFDP1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
MYO1B

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TUBA1B

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
SLC1A5

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TGM2

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
BARD1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TUBB4B

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
RRBP1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
ANXA2

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
PLK2

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
PSMC4

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
DGKE

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
CEBPD

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
HSPA2

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
RAB31

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TFAP2C

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
CD9

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TPM1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
LMO7

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
KLF5

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TRIB1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
HIST1H1C

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
DAAM1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
In [969]:
cor_plot_df_ic50
A data.frame: 25 × 4
genepvaluecorrwith
<chr><dbl><dbl><chr>
GTF2B 0.92975418-0.01651038Erlotinib
MSH6 0.56196312 0.10830159Erlotinib
TFDP1 0.01508307-0.43256213Erlotinib
MYO1B 0.28797688-0.19706211Erlotinib
TUBA1B 0.44648053 0.14186892Erlotinib
SLC1A5 0.49956199 0.12595750Erlotinib
TGM2 0.77647654-0.05314013Erlotinib
BARD1 0.39443438 0.15849550Erlotinib
TUBB4B 0.04517675-0.36231141Erlotinib
RRBP1 0.02976071-0.39072017Erlotinib
ANXA2 0.04216628-0.36716625Erlotinib
PLK2 0.21199387-0.23060607Erlotinib
PSMC4 0.48109570 0.13138846Erlotinib
DGKE 0.81497566 0.04380999Erlotinib
CEBPD 0.94862159 0.01206895Erlotinib
HSPA2 0.39207702 0.15927702Erlotinib
RAB31 0.15069316 0.26435422Erlotinib
TFAP2C 0.29978271-0.19239185Erlotinib
CD9 0.12979298-0.27811604Erlotinib
TPM1 0.76846900 0.05509583Erlotinib
LMO7 0.10374793-0.29777327Erlotinib
KLF5 0.26370749-0.20706362Erlotinib
TRIB1 0.28921055-0.19656845Erlotinib
HIST1H1C0.50095518-0.12555192Erlotinib
DAAM1 0.02277635-0.40778755Erlotinib
In [970]:
pvalue_list <- c()
corr_list <- c()
gene_list <- drivers$V1[drivers$V1 %in% CCLE_drug_exp$GENE_SYMBOLS]

CCLE_surv <-  CCLE_select %>% filter(Compound == 'Lapatinib') %>%
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`))

for ( gene in gene_list){
    message(gene)
    cor_df <- CCLE_drug_exp %>% dplyr::filter(GENE_SYMBOLS == gene) %>% t() %>% as.data.frame()%>% 
                mutate(cell_line = colnames(CCLE_drug_exp)) %>%
                mutate(exp = as.numeric(CCLE_drug_exp[gene,])) %>%
                dplyr::select(c('exp','cell_line')) %>%
                merge(y =CCLE_surv,by = 'cell_line',all = F)


    pvalue_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method =  'pearson')[[3]] %>% c(pvalue_list,.)
    corr_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method =  'pearson')[[4]] %>% c(corr_list,.)
}

cor_plot_df_ic50_2 <- data.frame(gene = gene_list,pvalue= pvalue_list,corr = corr_list)
GTF2B

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
MSH6

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TFDP1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
MYO1B

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TUBA1B

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
SLC1A5

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TGM2

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
BARD1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TUBB4B

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
RRBP1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
ANXA2

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
PLK2

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
PSMC4

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
DGKE

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
CEBPD

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
HSPA2

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
RAB31

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TFAP2C

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
CD9

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TPM1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
LMO7

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
KLF5

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TRIB1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
HIST1H1C

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
DAAM1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
In [967]:
pvalue_list <- c()
corr_list <- c()
gene_list <- drivers$V1[drivers$V1 %in% CCLE_drug_exp$GENE_SYMBOLS]

CCLE_surv <-  CCLE_select %>% filter(Compound == 'ZD-6474') %>%
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`))

for ( gene in gene_list){
    message(gene)
    cor_df <- CCLE_drug_exp %>% dplyr::filter(GENE_SYMBOLS == gene) %>% t() %>% as.data.frame()%>% 
                mutate(cell_line = colnames(CCLE_drug_exp)) %>%
                mutate(exp = as.numeric(CCLE_drug_exp[gene,])) %>%
                dplyr::select(c('exp','cell_line')) %>%
                merge(y =CCLE_surv,by = 'cell_line',all = F)


    pvalue_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method =  'pearson')[[3]] %>% c(pvalue_list,.)
    corr_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method =  'pearson')[[4]] %>% c(corr_list,.)
}

cor_plot_df_ic50_3 <- data.frame(gene = gene_list,pvalue= pvalue_list,corr = corr_list)
GTF2B

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
MSH6

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TFDP1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
MYO1B

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TUBA1B

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
SLC1A5

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TGM2

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
BARD1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TUBB4B

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
RRBP1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
ANXA2

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
PLK2

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
PSMC4

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
DGKE

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
CEBPD

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
HSPA2

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
RAB31

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TFAP2C

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
CD9

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TPM1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
LMO7

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
KLF5

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
TRIB1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
HIST1H1C

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
DAAM1

Warning message:
“There was 1 warning in `mutate()`.
ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`.
Caused by warning:
! NAs introduced by coercion”
In [971]:
cor_plot_df_ic50$with = 'Erlotinib'
cor_plot_df_ic50_2$with = 'Lapatinib'
cor_plot_df_ic50_3$with = 'ZD-6474'
cor_plot_df <- rbind(cor_plot_df_ic50,cor_plot_df_ic50_2,cor_plot_df_ic50_3)
In [972]:
cor_plot_df
A data.frame: 75 × 4
genepvaluecorrwith
<chr><dbl><dbl><chr>
GTF2B 0.92975418-0.01651038Erlotinib
MSH6 0.56196312 0.10830159Erlotinib
TFDP1 0.01508307-0.43256213Erlotinib
MYO1B 0.28797688-0.19706211Erlotinib
TUBA1B 0.44648053 0.14186892Erlotinib
SLC1A5 0.49956199 0.12595750Erlotinib
TGM2 0.77647654-0.05314013Erlotinib
BARD1 0.39443438 0.15849550Erlotinib
TUBB4B 0.04517675-0.36231141Erlotinib
RRBP1 0.02976071-0.39072017Erlotinib
ANXA2 0.04216628-0.36716625Erlotinib
PLK2 0.21199387-0.23060607Erlotinib
PSMC4 0.48109570 0.13138846Erlotinib
DGKE 0.81497566 0.04380999Erlotinib
CEBPD 0.94862159 0.01206895Erlotinib
HSPA2 0.39207702 0.15927702Erlotinib
RAB31 0.15069316 0.26435422Erlotinib
TFAP2C 0.29978271-0.19239185Erlotinib
CD9 0.12979298-0.27811604Erlotinib
TPM1 0.76846900 0.05509583Erlotinib
LMO7 0.10374793-0.29777327Erlotinib
KLF5 0.26370749-0.20706362Erlotinib
TRIB1 0.28921055-0.19656845Erlotinib
HIST1H1C0.50095518-0.12555192Erlotinib
DAAM1 0.02277635-0.40778755Erlotinib
GTF2B 0.88778965 0.02642410Lapatinib
MSH6 0.65733708 0.08294254Lapatinib
TFDP1 0.27349904-0.20295966Lapatinib
MYO1B 0.08181342-0.31746279Lapatinib
TUBA1B 0.26284187 0.20743117Lapatinib
⋮⋮⋮⋮
LMO7 0.14937270-0.26518112Lapatinib
KLF5 0.05505435-0.34800671Lapatinib
TRIB1 0.23498666-0.21971159Lapatinib
HIST1H1C0.58040949-0.10326080Lapatinib
DAAM1 0.01127564-0.44904895Lapatinib
GTF2B 0.41813530-0.15078139ZD-6474
MSH6 0.50852074 0.12335927ZD-6474
TFDP1 0.22806822-0.22291079ZD-6474
MYO1B 0.57433335-0.10491314ZD-6474
TUBA1B 0.63365707-0.08908780ZD-6474
SLC1A5 0.28427088 0.19855327ZD-6474
TGM2 0.24046724-0.21722184ZD-6474
BARD1 0.74869276-0.05995084ZD-6474
TUBB4B 0.20902769-0.23206919ZD-6474
RRBP1 0.16022545-0.25853461ZD-6474
ANXA2 0.04054861-0.36989069ZD-6474
PLK2 0.05410956-0.34928249ZD-6474
PSMC4 0.57194067-0.10556595ZD-6474
DGKE 0.42530412-0.14849669ZD-6474
CEBPD 0.38671226-0.16106570ZD-6474
HSPA2 0.29109490-0.19581700ZD-6474
RAB31 0.55915481 0.10907555ZD-6474
TFAP2C 0.09441545-0.30572149ZD-6474
CD9 0.11010731-0.29266084ZD-6474
TPM1 0.15491601-0.26174438ZD-6474
LMO7 0.55124632-0.11126481ZD-6474
KLF5 0.53580016-0.11558346ZD-6474
TRIB1 0.89498519 0.02471996ZD-6474
HIST1H1C0.30020616-0.19222652ZD-6474
DAAM1 0.09969599-0.30115493ZD-6474
In [973]:
cor_plot_df$p_status <- cor_plot_df$pvalue <= 0.05
In [974]:
p2 <- ggplot(data = cor_plot_df)+
    geom_point(aes(x = with,y = gene,size=abs(corr),color = p_status))+
    geom_text(aes(x = with,y = gene,label = round(corr,2)),color = 'white')+
    scale_size_continuous(range = c(8, 15))+
    scale_colour_manual(values = c('TRUE'= "#8C0303", 'FALSE' = "#2A398C"))+
    theme_bw()
p2
No description has been provided for this image
In [1010]:
gene_cor_plot_df <- gene_cor_plot_df %>% filter(with == 'IC50')
gene_cor_plot_df$with = 'Osimertinib'
gene_cor_plot_df <- rbind(cor_plot_df,gene_cor_plot_df)
In [1011]:
p3 <- ggplot(data = gene_cor_plot_df)+
    geom_point(aes(x = with,y = gene,size=abs(corr),color = p_status))+
    geom_text(aes(x = with,y = gene,label = round(corr,2)),color = 'white')+
    scale_size_continuous(range = c(8, 15))+
    scale_colour_manual(values = c('TRUE'= "#8C0303", 'FALSE' = "#2A398C"))+
    theme_bw()
p3
No description has been provided for this image
In [1012]:
table(gene_cor_plot_df$gene)
table(gene_cor_plot_df$with)
   ANXA2    BARD1      CD9    CEBPD    DAAM1     DGKE    GTF2B HIST1H1C 
       4        4        4        4        4        4        4        4 
   HSPA2     KLF5     LMO7     MSH6    MYO1B     PLK2    PSMC4    RAB31 
       4        4        4        4        4        4        4        4 
   RRBP1   SLC1A5   TFAP2C    TFDP1     TGM2     TPM1    TRIB1   TUBA1B 
       4        4        4        4        4        4        4        4 
  TUBB4B 
       4 
  Erlotinib   Lapatinib Osimertinib     ZD-6474 
         25          25          25          25 
In [1019]:
gene_cor_plot_mtx <-  gene_cor_plot_df %>% dplyr::select(c('gene','corr','with')) %>%  tidyr::spread(key = 'with',value = 'corr')
rownames(gene_cor_plot_mtx) <- gene_cor_plot_mtx$gene
gene_cor_plot_mtx <- gene_cor_plot_mtx[,2:ncol(gene_cor_plot_mtx)] %>% as.matrix()
In [1023]:
gene_cor_plot_mtx
A matrix: 25 × 4 of type dbl
ErlotinibLapatinibOsimertinibZD-6474
ANXA2-0.36716625-0.54191262 0.004082954-0.36989069
BARD1 0.15849550 0.18913780-0.097575263-0.05995084
CD9-0.27811604-0.37724916-0.088261276-0.29266084
CEBPD 0.01206895 0.04715317-0.106193334-0.16106570
DAAM1-0.40778755-0.44904895-0.518766520-0.30115493
DGKE 0.04380999-0.01012269 0.115840427-0.14849669
GTF2B-0.01651038 0.02642410 0.004675303-0.15078139
HIST1H1C-0.12555192-0.10326080 0.092923887-0.19222652
HSPA2 0.15927702 0.16280258-0.012426096-0.19581700
KLF5-0.20706362-0.34800671-0.308475948-0.11558346
LMO7-0.29777327-0.26518112 0.095621202-0.11126481
MSH6 0.10830159 0.08294254-0.041415908 0.12335927
MYO1B-0.19706211-0.31746279-0.316363859-0.10491314
PLK2-0.23060607-0.34227929-0.098415166-0.34928249
PSMC4 0.13138846 0.02879852 0.170478791-0.10556595
RAB31 0.26435422 0.30693390-0.054640531 0.10907555
RRBP1-0.39072017-0.46196864-0.155403122-0.25853461
SLC1A5 0.12595750 0.07289740 0.100480841 0.19855327
TFAP2C-0.19239185-0.30826752-0.165888109-0.30572149
TFDP1-0.43256213-0.20295966 0.138099376-0.22291079
TGM2-0.05314013-0.15071512-0.014999345-0.21722184
TPM1 0.05509583 0.01603657 0.138427871-0.26174438
TRIB1-0.19656845-0.21971159 0.001650178 0.02471996
TUBA1B 0.14186892 0.20743117 0.163705070-0.08908780
TUBB4B-0.36231141-0.26564096-0.080657379-0.23206919
In [1061]:
gene_cor_p_mtx <-  gene_cor_plot_df %>% dplyr::select(c('gene','pvalue','with')) %>%  tidyr::spread(key = 'with',value = 'pvalue')
rownames(gene_cor_p_mtx) <- gene_cor_p_mtx$gene
gene_cor_p_mtx <- gene_cor_p_mtx[,2:ncol(gene_cor_p_mtx)] %>% as.matrix()
In [1062]:
gene_cor_p_mtx
A matrix: 25 × 4 of type dbl
ErlotinibLapatinibOsimertinibZD-6474
ANXA20.042166280.0016391999.750867e-010.04054861
BARD10.394434380.3081865724.543944e-010.74869276
CD90.129792980.0364272334.987845e-010.11010731
CEBPD0.948621590.8011269354.153328e-010.38671226
DAAM10.022776350.0112756391.843256e-050.09969599
DGKE0.814975660.9568985583.739904e-010.42530412
GTF2B0.929754180.8877896479.714737e-010.41813530
HIST1H1C0.500955180.5804094904.762866e-010.30020616
HSPA20.392077020.3815436629.242777e-010.29109490
KLF50.263707490.0550543511.557443e-020.53580016
LMO70.103747930.1493726964.635234e-010.55124632
MSH60.561963120.6573370787.513098e-010.50852074
MYO1B0.287976880.0818134211.299438e-020.57433335
PLK20.211993870.0594573654.505011e-010.05410956
PSMC40.481095700.8777785851.889890e-010.57194067
RAB310.150693160.0930498146.757715e-010.55915481
RRBP10.029760710.0088889522.317320e-010.16022545
SLC1A50.499561990.6967468384.410045e-010.28427088
TFAP2C0.299782710.0915650462.013561e-010.09441545
TFDP10.015083070.2734990442.885206e-010.22806822
TGM20.776476540.4183422619.086580e-010.24046724
TPM10.768469000.9317653632.873641e-010.15491601
TRIB10.289210550.2349866609.899296e-010.89498519
TUBA1B0.446480530.2628418712.074346e-010.63365707
TUBB4B0.045176750.1486420075.366215e-010.20902769
In [1074]:
library(ComplexHeatmap)
library(circlize)
col_fun = colorRamp2(c(-0.25,0,0.25),c('#D28130','white','#559073'))
cell_fun = function(j, i, x, y, width, height, fill) {
        if (gene_cor_p_mtx[i,j] <=0.05){
            grid.text(sprintf("%.3f", gene_cor_plot_mtx[i, j]), x, y, gp = gpar(fontsize = 10,col = '#A11715', fontface = "bold"))
        }else{
            grid.text(sprintf("%.3f", gene_cor_plot_mtx[i, j]), x, y, gp = gpar(fontsize = 10,col = 'black'))
        }
}
In [1088]:
p <- Heatmap(gene_cor_plot_mtx,
        cluster_rows = T,
        #clustering_distance_rows = "pearson",
        show_row_dend =F,
        cluster_columns = T,
        row_names_side = "left",
        show_column_dend  =F,
        row_names_gp = gpar(fontsize = 10),
        column_names_gp = gpar(fontsize = 10),
        col = col_fun,
        cell_fun = cell_fun,
        rect_gp = gpar(col = "white", lwd = 2),
        column_title  = 'Drugs target EGFR',
        column_title_gp = gpar(fontsize = 20, fontface = "bold"),
        row_title = "Driver genes", row_title_rot = 90,
        row_title_gp = gpar(fontsize = 20, fontface = "bold"),)
p
No description has been provided for this image
In [1089]:
pdf(file = "/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_EGFR_drugs_heatmap.pdf",width = 8/1.5,height = 12/1.5)
p
dev.off()
png: 2
In [1104]:
tgene_cor_p_mtx <- t(gene_cor_p_mtx)
tgene_cor_plot_mtx <- t(gene_cor_plot_mtx)
In [1160]:
drivers <- drivers %>% as.data.frame()
rownames(drivers) <- drivers$V1
directions_cal <- drivers%>% dplyr::select('directions_cal')

weight_cal <- drivers%>% dplyr::select('weight_shap_total_mean') %>% normalize()
In [1179]:
col_fun = colorRamp2(c(-0.25,0,0.25),c('#D28130','white','#559073'))
cell_fun = function(j, i, x, y, width, height, fill) {
        if (tgene_cor_p_mtx[i,j] <=0.05){
            grid.text(sprintf("%.3f", tgene_cor_plot_mtx[i, j]), x, y, gp = gpar(fontsize = 10,col = '#A11715', fontface = "bold"))
        }else{
            grid.text(sprintf("%.3f", tgene_cor_plot_mtx[i, j]), x, y, gp = gpar(fontsize = 10,col = 'black'))
        }
}
col = list(Direction = c("-1" = "#FF09A4", "1" = "#32510A"))
#library(circlize)
#col_fun2 = colorRamp2(c(-2, -1, 0,1), c("#A6335D", "#ED6193", "#C6C6C6","#91374B"))
ha = HeatmapAnnotation(Direction = as.character(directions_cal[colnames(tgene_cor_plot_mtx),]),
                       `Normalized SHAP weight` = anno_barplot(weight_cal[colnames(tgene_cor_plot_mtx),],gp = gpar(fill ='#779CD2') ),
                       col = col,
                       gp = gpar(col = "white",lty=1,
                                lwd = 6),
                       height = unit(25, "mm"),
                       gap = unit(c(1.5), "mm"))

p <- Heatmap(tgene_cor_plot_mtx,
        cluster_rows = T,
        #clustering_distance_rows = "pearson",
        show_row_dend =F,
        cluster_columns = T,
        row_names_side = "left",
        show_column_dend  =F,
        row_names_gp = gpar(fontsize = 10),
        column_names_gp = gpar(fontsize = 10),
        col = col_fun,
        cell_fun = cell_fun,
        rect_gp = gpar(col = "white", lwd = 2),
        column_title  = 'Drugs target EGFR',
        column_title_gp = gpar(fontsize = 20, fontface = "bold"),
        row_title = "Driver genes", row_title_rot = 90,
        row_title_gp = gpar(fontsize = 20, fontface = "bold"),
        bottom_annotation = ha,)
p
No description has been provided for this image
In [1181]:
pdf(file = "/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/H_sen_res_EGFR_drugs_heatmap.pdf",width = 21/1.5,height = 7/1.5)
p
dev.off()
png: 2

test sorce¶

GDSC¶

In [198]:
FUN1 <- function(x){
    sum(x*score_df$weight_shap_total_mean)
    #sum(x)
}

normalize <- function(v) {
  (v - min(v)) / (max(v) - min(v))
}
FUN2 <- function(x){
    sum(x*normalize(score_df$weight_shap_total_mean))
    #sum(x)
}
FUN3 <- function(x){
    mean(x*normalize(score_df$weight_shap_total_mean))
    #sum(x)
}
FUN4 <- function(x){
    mean(x*score_df$weight_shap_total_mean)
    #sum(x)
}
FUN5 <- function(x){
    sum(x*score_df$weight_shap_total_mean*score_df$directions_cal)
    #sum(x)
}
In [200]:
score_df <- merge(GDSC_select_exp,drivers,by.x = 'GENE_SYMBOLS',by.y = 'V1',all = F)
score <- apply(score_df[,2:ncol(GDSC_select_exp)],MARGIN  = 2,FUN = FUN5)
names(score) <- names(score) %>% substring(.,6)
GDSC_select$score = score[as.character(GDSC_select$COSMIC_ID)]
In [201]:
cor.test(GDSC_select$LN_IC50,GDSC_select$score,alternative = 'less',method =  'spearman')
cor.test(GDSC_select$AUC,GDSC_select$score,alternative = 'less',method =  'spearman')
	Spearman's rank correlation rho

data:  GDSC_select$LN_IC50 and GDSC_select$score
S = 49784, p-value = 0.00666
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.3163406 
	Spearman's rank correlation rho

data:  GDSC_select$AUC and GDSC_select$score
S = 48350, p-value = 0.01506
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.2784241 
In [18]:
ggplot(data = GDSC_select,aes(x=score,y=LN_IC50))+
geom_point(size=3,shape=21,color='black',fill='#03658C')+
geom_smooth(size=1.5,method=lm , color="#BA002B", fill="#69b3a2", se=TRUE) +
geom_text(aes(x=0,y=5,label='Cor = 0.3163406  \n P-value = 0.00666'),size=4,color = '#03658C')+
xlab('Drivers causal score')+
ylab('IC50 (uM)')+
theme_classic()
Warning message:
“Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.”
Warning message in geom_text(aes(x = 0, y = 5, label = "Cor = 0.3163406  \n P-value = 0.00666"), :
“All aesthetics have length 1, but the data has 61 rows.
ℹ Did you mean to use `annotate()`?”
`geom_smooth()` using formula = 'y ~ x'
No description has been provided for this image
In [19]:
corp <- ggplot(data = GDSC_select,aes(x=score,y=LN_IC50))+
geom_point(size=10,shape=21,stork=2,color='black',fill='#559073CC')+
geom_smooth(size=3,method=lm , color="#BA002B", fill="#69b3a2", se=TRUE) +
geom_text(aes(x=-0.005,y=5,label='Cor = 0.3163406  \n P-value = 0.00666'),size=12,color = 'black')+
xlab('Drivers causal score')+
ylab('IC50 (uM)')+
theme_classic()+
#scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=24),
    axis.title.x = element_text(vjust = -5),
    axis.title.y = element_text(vjust = 6),
   axis.text = element_text(face = 'italic',size=20,colour = 'black'),
   axis.text.x = element_text(angle = 60,vjust = 0.5),
    #axis.text.y = element_text(hjust = 8),
    axis.ticks = element_line(linewidth = 1.5),
   axis.ticks.length = unit(10,'points'),
   axis.line = element_line(linewidth = 1.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   panel.border = element_blank(),
   panel.grid = element_blank(),
   #panel.grid.major.x =element_line(),
   legend.key.size = unit(20,'points'),
   legend.text = element_text(face = 'italic',size=18),
   #legend.title = element_text(face = 'bold',size=18), 
   legend.title = element_blank(),
   legend.position = 'top'
      
)
corp
Warning message in geom_point(size = 10, shape = 21, stork = 2, color = "black", :
“Ignoring unknown parameters: `stork`”
Warning message in geom_text(aes(x = -0.005, y = 5, label = "Cor = 0.3163406  \n P-value = 0.00666"), :
“All aesthetics have length 1, but the data has 61 rows.
ℹ Did you mean to use `annotate()`?”
`geom_smooth()` using formula = 'y ~ x'
No description has been provided for this image
In [20]:
ggsave(plot = corp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_corp.pdf',
       width =16/1.5, height =16/1.5)
ggsave(plot = corp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_corp.png',
       width =16/1.5, height =16/1.5)
Warning message in geom_text(aes(x = -0.005, y = 5, label = "Cor = 0.3163406  \n P-value = 0.00666"), :
“All aesthetics have length 1, but the data has 61 rows.
ℹ Did you mean to use `annotate()`?”
`geom_smooth()` using formula = 'y ~ x'
Warning message in geom_text(aes(x = -0.005, y = 5, label = "Cor = 0.3163406  \n P-value = 0.00666"), :
“All aesthetics have length 1, but the data has 61 rows.
ℹ Did you mean to use `annotate()`?”
`geom_smooth()` using formula = 'y ~ x'
In [1205]:
# 计算分位数,这里我们分为三组,所以使用0%, 33%, 67% 和 100%的分位数
quantiles <- quantile(GDSC_select$score, probs = c(0, 1/3, 2/3, 1))

# 使用cut函数将score分组,并创建新列group
GDSC_select$group <- cut(GDSC_select$score, breaks = quantiles, 
                         labels = c("Low", "Medium", "High"), include.lowest = TRUE)

GDSC_select$group <- factor(GDSC_select$group,levels = c("High","Medium","Low"))
# 查看结果
head(GDSC_select)
A data.frame: 6 × 21
DATASETNLME_RESULT_IDNLME_CURVE_IDCOSMIC_IDCELL_LINE_NAMESANGER_MODEL_IDTCGA_DESCDRUG_IDDRUG_NAMEPUTATIVE_TARGET⋯COMPANY_IDWEBRELEASEMIN_CONCMAX_CONCLN_IC50AUCRMSEZ_SCOREscoregroup
<chr><dbl><dbl><dbl><chr><chr><chr><dbl><chr><chr>⋯<dbl><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><fct>
1GDSC234315952962687777Calu-3 SIDM00922LUAD1919OsimertinibEGFR⋯1046Y0.0010011 0.1050570.8279930.098212-1.211272 0.006858576High
2GDSC234315953984687798NCI-H1623SIDM00747LUAD1919OsimertinibEGFR⋯1046Y0.0010011 0.1965950.8681510.052729-1.139565-0.004671004Low
3GDSC234315954155687799NCI-H1648SIDM00746LUAD1919OsimertinibEGFR⋯1046Y0.0010011-0.7015920.8008710.093298-1.843171 0.005754738High
4GDSC234315954434687800NCI-H1650SIDM00745LUAD1919OsimertinibEGFR⋯1046Y0.0010011 0.4078720.8448570.134400-0.974058 0.001806272Medium
5GDSC234315954715687802NCI-H1693SIDM00742LUAD1919OsimertinibEGFR⋯1046Y0.0010011 1.1113000.8610170.104960-0.423019-0.001409169Medium
6GDSC234315955064687807NCI-H1838SIDM00769LUAD1919OsimertinibEGFR⋯1046Y0.0010011 3.4626170.9601850.020880 1.418915-0.008347880Low
In [1206]:
p <- ggplot(data = GDSC_select,aes(x=group,y=LN_IC50,color=group))+
geom_boxplot(size=1.5)+
ggbeeswarm::geom_quasirandom(method = "smiley",size=6,alpha=0.7)+

theme_classic()+#设置两轴白底主体

theme(line = element_line(size=1.5),#坐标轴除夕及文字大小
     axis.title = element_text(size=18),
     axis.text = element_text(size=14),
     axis.ticks = element_line(size=1.5),
     axis.ticks.length = unit(0.3, "cm"))+

ggsci::scale_color_npg()

p
No description has been provided for this image
In [1207]:
# 比较Low组与Medium组
low_medium_t_test <- t.test(LN_IC50 ~ group, data = GDSC_select,alternative ='less', subset = group %in% c("Low", "Medium"))
print("Low vs Medium:")
print(low_medium_t_test)

# 比较Low组与High组
low_high_t_test <- t.test(LN_IC50 ~ group, data = GDSC_select,alternative ='less', subset = group %in% c("Low", "High"))
print("Low vs High:")
print(low_high_t_test)

# 比较Medium组与High组
medium_high_t_test <- t.test(LN_IC50 ~ group, data = GDSC_select,alternative ='less',  subset = group %in% c("Medium", "High"))
print("Medium vs High:")
print(medium_high_t_test)
[1] "Low vs Medium:"

	Welch Two Sample t-test

data:  LN_IC50 by group
t = -2.2825, df = 28.817, p-value = 0.01502
alternative hypothesis: true difference in means between group Medium and group Low is less than 0
95 percent confidence interval:
      -Inf -0.315564
sample estimates:
mean in group Medium    mean in group Low 
            1.084215             2.319713 

[1] "Low vs High:"

	Welch Two Sample t-test

data:  LN_IC50 by group
t = -2.4293, df = 28.482, p-value = 0.01084
alternative hypothesis: true difference in means between group High and group Low is less than 0
95 percent confidence interval:
       -Inf -0.4008829
sample estimates:
mean in group High  mean in group Low 
         0.9841209          2.3197129 

[1] "Medium vs High:"

	Welch Two Sample t-test

data:  LN_IC50 by group
t = -0.14618, df = 37.985, p-value = 0.4423
alternative hypothesis: true difference in means between group High and group Medium is less than 0
95 percent confidence interval:
     -Inf 1.054361
sample estimates:
  mean in group High mean in group Medium 
           0.9841209            1.0842150 

In [1208]:
# 比较Low组与Medium组
low_medium_t_test <- wilcox.test(LN_IC50 ~ group, data = GDSC_select,alternative ='less',  subset = group %in% c("Low", "Medium"))
print("Low vs Medium:")
print(low_medium_t_test)

# 比较Low组与High组
low_high_t_test <- wilcox.test(LN_IC50 ~ group, data = GDSC_select,alternative ='less',  subset = group %in% c("Low", "High"))
print("Low vs High:")
print(low_high_t_test)

# 比较Medium组与High组
medium_high_t_test <- wilcox.test(LN_IC50 ~ group, data = GDSC_select,alternative ='less',  subset = group %in% c("Medium", "High"))
print("Medium vs High:")
print(medium_high_t_test)
[1] "Low vs Medium:"

	Wilcoxon rank sum exact test

data:  LN_IC50 by group
W = 135, p-value = 0.02565
alternative hypothesis: true location shift is less than 0

[1] "Low vs High:"

	Wilcoxon rank sum exact test

data:  LN_IC50 by group
W = 125, p-value = 0.0132
alternative hypothesis: true location shift is less than 0

[1] "Medium vs High:"

	Wilcoxon rank sum exact test

data:  LN_IC50 by group
W = 195, p-value = 0.4521
alternative hypothesis: true location shift is less than 0

In [1209]:
p <- ggplot(data = GDSC_select,aes(x=group,y=LN_IC50))+
geom_boxplot(aes(fill=group),size=1.5)+
#ggbeeswarm::geom_quasirandom(aes(color=group),method = "smiley",size=6,alpha=0.7)+
ggsignif::geom_signif(aes(x=group,y=LN_IC50),comparisons =  list(c('Low', "Medium"),c('Low', "High"),c('Medium', "High")),step_increase = 0.15,map_signif_level = F,test = 't.test',test.args = c('greater')) +

theme_classic()+#设置两轴白底主体

theme(line = element_line(size=1.5),#坐标轴除夕及文字大小
     axis.title = element_text(size=18),
     axis.text = element_text(size=14),
     axis.ticks = element_line(size=1.5),
     axis.ticks.length = unit(0.3, "cm"))+

#ggsci::scale_color_npg()+
ggsci::scale_fill_npg()

p
No description has been provided for this image
In [1220]:
# 计算分位数,这里我们分为三组,所以使用0%, 33%, 67% 和 100%的分位数
quantiles <- quantile(GDSC_select$score, probs = c(0, 0.5, 1))

# 使用cut函数将score分组,并创建新列group
GDSC_select$group2 <- cut(GDSC_select$score, breaks = quantiles, 
                         labels = c("Low", "High"), include.lowest = TRUE)

GDSC_select$group2 <- factor(GDSC_select$group2,levels = c("High","Low"))
# 查看结果
head(GDSC_select)
A data.frame: 6 × 22
DATASETNLME_RESULT_IDNLME_CURVE_IDCOSMIC_IDCELL_LINE_NAMESANGER_MODEL_IDTCGA_DESCDRUG_IDDRUG_NAMEPUTATIVE_TARGET⋯WEBRELEASEMIN_CONCMAX_CONCLN_IC50AUCRMSEZ_SCOREscoregroupgroup2
<chr><dbl><dbl><dbl><chr><chr><chr><dbl><chr><chr>⋯<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><fct><fct>
1GDSC234315952962687777Calu-3 SIDM00922LUAD1919OsimertinibEGFR⋯Y0.0010011 0.1050570.8279930.098212-1.211272 0.006858576High High
2GDSC234315953984687798NCI-H1623SIDM00747LUAD1919OsimertinibEGFR⋯Y0.0010011 0.1965950.8681510.052729-1.139565-0.004671004Low Low
3GDSC234315954155687799NCI-H1648SIDM00746LUAD1919OsimertinibEGFR⋯Y0.0010011-0.7015920.8008710.093298-1.843171 0.005754738High High
4GDSC234315954434687800NCI-H1650SIDM00745LUAD1919OsimertinibEGFR⋯Y0.0010011 0.4078720.8448570.134400-0.974058 0.001806272MediumHigh
5GDSC234315954715687802NCI-H1693SIDM00742LUAD1919OsimertinibEGFR⋯Y0.0010011 1.1113000.8610170.104960-0.423019-0.001409169MediumLow
6GDSC234315955064687807NCI-H1838SIDM00769LUAD1919OsimertinibEGFR⋯Y0.0010011 3.4626170.9601850.020880 1.418915-0.008347880Low Low
In [1221]:
p <- ggplot(data = GDSC_select,aes(x=group2,y=LN_IC50))+
geom_boxplot(aes(fill=group2),size=1.5)+
#ggbeeswarm::geom_quasirandom(aes(color=group),method = "smiley",size=6,alpha=0.7)+
ggsignif::geom_signif(aes(x=group2,y=LN_IC50),comparisons =  list(c('Low', "High")),map_signif_level = F,test = 't.test',test.args = c('greater')) +

theme_classic()+#设置两轴白底主体

theme(line = element_line(size=1.5),#坐标轴除夕及文字大小
     axis.title = element_text(size=18),
     axis.text = element_text(size=14),
     axis.ticks = element_line(size=1.5),
     axis.ticks.length = unit(0.3, "cm"))+

#ggsci::scale_color_npg()+
ggsci::scale_fill_npg()

p
No description has been provided for this image
In [1247]:
score_boxp <- ggplot(data = GDSC_select,aes(x=group2,y=LN_IC50,fill=group2))+
stat_boxplot(geom = "errorbar",linewidth=1.5,width = 0.5)+
#geom_violin(outliers = F,linewidth=1.5,color='black')+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
#geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
#ggbeeswarm::geom_quasirandom(method = "smiley",size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
                      comparisons =  list(c('Low', "High")),
                    step_increase = 0.15,
                    annotations= c('P-value = 0.021'),
                    textsize = 8,size=1.5,vjust=0) +

theme_classic()+
xlab('Group by CRS')+
ylab('IC50 (uM)')+
ggtitle("GDSC")+
theme_bw()+
#scale_fill_manual(values = c('T14 T0'= '#559073FF','T14 T7'='#D28130FF'))+
ggsci::scale_fill_npg()+
ggsci::scale_color_npg()+
#scale_color_manual(values = c('T14 T0'= '#559073FF','T14 T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5),
    axis.title.y = element_text(vjust = 6),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(angle = 60,vjust = 0.5),
    #axis.text.y = element_text(hjust = 8),
    axis.ticks = element_line(linewidth = 1.5),
   axis.ticks.length = unit(10,'points'),
   axis.line = element_line(linewidth = 1.5),
   plot.title = element_text(face = 'bold',size=28,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   panel.border = element_blank(),
   panel.grid = element_blank(),
   #panel.grid.major.x =element_line(),
   legend.key.size = unit(20,'points'),
   legend.text = element_text(face = 'italic',size=18),
   #legend.title = element_text(face = 'bold',size=18), 
   legend.title = element_blank(),
   legend.position = 'none')

score_boxp
No description has been provided for this image
In [1248]:
ggsave(plot = score_boxp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_score_boxp.pdf',
       width =14/1.5, height =14/1.5)
ggsave(plot = score_boxp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_score_boxp.png',
       width =16/1.5, height =16/1.5)

CCLE¶

In [209]:
rn <- CCLE_drug_exp$Description
CCLE_drug_exp <- CCLE_drug_exp[,2:ncol(CCLE_drug_exp)]
rownames(CCLE_drug_exp) <- rn
CCLE_drug_exp <- log2(CCLE_drug_exp)
CCLE_drug_exp$V1 = rownames(CCLE_drug_exp)

score_df <- merge(CCLE_drug_exp,drivers,by = 'V1',all = F)

score <- apply(score_df[,2:32],MARGIN  = 2,FUN = FUN1)
In [210]:
drug_res <- CCLE_select %>% 
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`)) %>% filter(cell_line %in% names(score))
drug_res$score = score[as.character(drug_res$cell_line)]
In [211]:
cor.test(drug_res$`IC50 (uM)`,drug_res$score,alternative = 'less',method =  'spearman')
cor.test(drug_res$`EC50 (uM)`,drug_res$score,alternative = 'less',method =  'spearman')
Warning message in cor.test.default(drug_res$`IC50 (uM)`, drug_res$score, alternative = "less", :
“Cannot compute exact p-value with ties”
	Spearman's rank correlation rho

data:  drug_res$`IC50 (uM)` and drug_res$score
S = 180450, p-value = 0.0003379
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.3462008 
Warning message in cor.test.default(drug_res$`EC50 (uM)`, drug_res$score, alternative = "less", :
“Cannot compute exact p-value with ties”
	Spearman's rank correlation rho

data:  drug_res$`EC50 (uM)` and drug_res$score
S = 44641, p-value = 0.08214
alternative hypothesis: true rho is less than 0
sample estimates:
       rho 
-0.1803435 
In [212]:
ggplot(data = drug_res,aes(x=score,y=`IC50 (uM)`))+
geom_point(size=3,shape=21,color='black',fill='#03658C')+
geom_smooth(size=1.5,method=lm , color="#BA002B", fill="#69b3a2", se=TRUE) +
#geom_text(aes(x=0,y=5,label='Cor = 0.3163406  \n P-value = 0.00666'),size=4,color = '#03658C')+
xlab('Drivers causal score')+
ylab('IC50 (uM)')+
theme_classic()
Warning message:
“Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.”
`geom_smooth()` using formula = 'y ~ x'
No description has been provided for this image
In [281]:
corp <- ggplot(data = drug_res,aes(x=score,y=`IC50 (uM)`))+
geom_point(size=10,shape=21,stork=2,color='black',fill='#559073CC')+
geom_smooth(size=3,method=lm , color="#BA002B", fill="#69b3a2", se=TRUE) +
geom_text(aes(x=0.12,y=11,label='Cor = -0.3462  \n P-value = 0.0003379'),size=12,color = 'black')+
xlab('Drivers causal score')+
ylab('IC50 (uM)')+
theme_classic()+
#scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=24),
    axis.title.x = element_text(vjust = -5),
    axis.title.y = element_text(vjust = 6),
   axis.text = element_text(face = 'italic',size=20,colour = 'black'),
   axis.text.x = element_text(angle = 60,vjust = 0.5),
    #axis.text.y = element_text(hjust = 8),
    axis.ticks = element_line(linewidth = 1.5),
   axis.ticks.length = unit(10,'points'),
   axis.line = element_line(linewidth = 1.5),
   plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   panel.border = element_blank(),
   panel.grid = element_blank(),
   #panel.grid.major.x =element_line(),
   legend.key.size = unit(20,'points'),
   legend.text = element_text(face = 'italic',size=18),
   #legend.title = element_text(face = 'bold',size=18), 
   legend.title = element_blank(),
   legend.position = 'top'
      
)
corp
Warning message in geom_point(size = 10, shape = 21, stork = 2, color = "black", :
“Ignoring unknown parameters: `stork`”
Warning message in geom_text(aes(x = 0.12, y = 11, label = "Cor = -0.3462  \n P-value = 0.0003379"), :
“All aesthetics have length 1, but the data has 93 rows.
ℹ Did you mean to use `annotate()`?”
`geom_smooth()` using formula = 'y ~ x'
No description has been provided for this image
In [282]:
ggsave(plot = corp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_corpCCLE.pdf',
       width =16/1.5, height =16/1.5)
ggsave(plot = corp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_corpCCLE.png',
       width =16/1.5, height =16/1.5)
Warning message in geom_text(aes(x = 0.12, y = 11, label = "Cor = -0.3462  \n P-value = 0.0003379"), :
“All aesthetics have length 1, but the data has 93 rows.
ℹ Did you mean to use `annotate()`?”
`geom_smooth()` using formula = 'y ~ x'
Warning message in geom_text(aes(x = 0.12, y = 11, label = "Cor = -0.3462  \n P-value = 0.0003379"), :
“All aesthetics have length 1, but the data has 93 rows.
ℹ Did you mean to use `annotate()`?”
`geom_smooth()` using formula = 'y ~ x'
In [ ]:

In [1243]:
# 计算分位数,这里我们分为三组,所以使用0%, 33%, 67% 和 100%的分位数
#quantiles <- quantile(drug_res$score, probs = c(0, 1/3, 2/3, 1))

# 使用cut函数将score分组,并创建新列group
drug_res$group <- cut(drug_res$`IC50 (uM)`, breaks = c(-Inf,8,Inf), 
                         labels = c("Sensitive", "Resistance"), include.lowest = TRUE,right=FALSE)

drug_res$group <- factor(drug_res$group,levels = c("Resistance","Sensitive"))
# 查看结果
head(drug_res)
A data.table: 6 × 5
cell_lineIC50 (uM)EC50 (uM)scoregroup
<chr><dbl><dbl><dbl><fct>
A549_LUNG 4.2272468.6927160.1348375Sensitive
A549_LUNG 8.0000002.0604240.1348375Resistance
A549_LUNG 8.0000002.3947180.1348375Resistance
CALU6_LUNG8.000000 NA0.1208364Resistance
CALU6_LUNG8.000000 NA0.1208364Resistance
CALU6_LUNG7.8079848.7041030.1208364Sensitive
In [1244]:
p <- ggplot(data = drug_res,aes(x=group,y=score))+
geom_boxplot(aes(fill=group),size=1.5)+
#ggbeeswarm::geom_quasirandom(aes(color=group),method = "smiley",size=6,alpha=0.7)+
ggsignif::geom_signif(aes(x=group,y=score),comparisons =  list(c('Sensitive', "Resistance")),map_signif_level = F,test = 't.test',test.args = c('greater')) +

theme_classic()+#设置两轴白底主体

theme(line = element_line(size=1.5),#坐标轴除夕及文字大小
     axis.title = element_text(size=18),
     axis.text = element_text(size=14),
     axis.ticks = element_line(size=1.5),
     axis.ticks.length = unit(0.3, "cm"))+

#ggsci::scale_color_npg()+
ggsci::scale_fill_npg()

p
No description has been provided for this image
In [1253]:
response_boxp <- ggplot(data = drug_res,aes(x=group,y=score,fill=group))+
stat_boxplot(geom = "errorbar",linewidth=1.5,width = 0.5)+
#geom_violin(outliers = F,linewidth=1.5,color='black')+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
#geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
#ggbeeswarm::geom_quasirandom(method = "smiley",size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
                      comparisons =  list(c('Sensitive', "Resistance")),
                    step_increase = 0.15,
                    annotations= c('P-value = 5.1e-05'),
                    textsize = 8,size=1.5,vjust=0) +

theme_classic()+
xlab('Drug response')+
ylab('CRS')+
ggtitle("CCLE")+
theme_bw()+
#scale_fill_manual(values = c('T14 T0'= '#559073FF','T14 T7'='#D28130FF'))+
ggsci::scale_fill_npg()+
ggsci::scale_color_npg()+
#scale_color_manual(values = c('T14 T0'= '#559073FF','T14 T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
    axis.title.x = element_text(vjust = -5),
    axis.title.y = element_text(vjust = 6),
   axis.text = element_text(face = 'italic',size=18,colour = 'black'),
   axis.text.x = element_text(angle = 0,vjust = 0.5),
    #axis.text.y = element_text(hjust = 8),
    axis.ticks = element_line(linewidth = 1.5),
   axis.ticks.length = unit(10,'points'),
   axis.line = element_line(linewidth = 1.5),
   plot.title = element_text(face = 'bold',size=28,hjust = 0.5,vjust = 8),
   plot.margin = margin(50,50,50,50),
   panel.border = element_blank(),
   panel.grid = element_blank(),
   #panel.grid.major.x =element_line(),
   legend.key.size = unit(20,'points'),
   legend.text = element_text(face = 'italic',size=18),
   #legend.title = element_text(face = 'bold',size=18), 
   legend.title = element_blank(),
   legend.position = 'none')

response_boxp
No description has been provided for this image
In [1254]:
ggsave(plot = response_boxp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_response_boxp.pdf',
       width =14/1.5, height =14/1.5)
ggsave(plot = response_boxp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_response_boxp.png',
       width =16/1.5, height =16/1.5)
In [ ]:

In [330]:
drug_res
A data.table: 93 × 5
cell_lineIC50 (uM)EC50 (uM)scoregroup
<chr><dbl><dbl><dbl><fct>
A549_LUNG 4.227246288.692716260.1348375Sensitive
A549_LUNG 8.000000002.060424090.1348375Resistance
A549_LUNG 8.000000002.394717690.1348375Resistance
CALU6_LUNG 8.00000000 NA0.1208364Resistance
CALU6_LUNG 8.00000000 NA0.1208364Resistance
CALU6_LUNG 7.807984358.704103250.1208364Sensitive
HARA_LUNG 6.656740678.861800420.1230818Sensitive
HARA_LUNG 8.000000002.151573420.1230818Resistance
HARA_LUNG 8.000000001.001384740.1230818Resistance
HCC15_LUNG 8.00000000 NA0.1307040Resistance
HCC15_LUNG 8.000000002.197055820.1307040Resistance
HCC15_LUNG 8.00000000 NA0.1307040Resistance
HCC827_LUNG 0.37213399 NA0.1293782Sensitive
HCC827_LUNG 0.317406480.307156300.1293782Sensitive
HCC827_LUNG 0.038917920.029334970.1293782Sensitive
KNS62_LUNG 8.000000003.775160070.1167083Resistance
KNS62_LUNG 8.00000000 NA0.1167083Resistance
KNS62_LUNG 8.00000000 NA0.1167083Resistance
LCLC103H_LUNG8.00000000 NA0.1243597Resistance
LCLC103H_LUNG8.00000000 NA0.1243597Resistance
LCLC103H_LUNG8.00000000 NA0.1243597Resistance
LUDLU1_LUNG 2.31964210 NA0.1294483Sensitive
LUDLU1_LUNG 1.825987341.016152980.1294483Sensitive
LUDLU1_LUNG 1.652492170.336647270.1294483Sensitive
MORCPR_LUNG 8.000000001.098604320.1189764Resistance
MORCPR_LUNG 8.00000000 NA0.1189764Resistance
MORCPR_LUNG 8.00000000 NA0.1189764Resistance
NCIH1299_LUNG8.000000000.616384330.1219884Resistance
NCIH1299_LUNG8.00000000 NA0.1219884Resistance
NCIH1299_LUNG8.000000008.627411370.1219884Resistance
⋮⋮⋮⋮⋮
NCIH2170_LUNG0.300980900.228014380.12365955Sensitive
NCIH2170_LUNG2.655644892.740792510.12365955Sensitive
NCIH2170_LUNG1.564355141.180775640.12365955Sensitive
NCIH2172_LUNG8.00000000 NA0.12834069Resistance
NCIH2172_LUNG8.00000000 NA0.12834069Resistance
NCIH2172_LUNG8.00000000 NA0.12834069Resistance
NCIH23_LUNG 8.000000008.230145350.12581737Resistance
NCIH23_LUNG 8.00000000 NA0.12581737Resistance
NCIH23_LUNG 8.00000000 NA0.12581737Resistance
NCIH322_LUNG 1.362128501.252795340.12767091Sensitive
NCIH322_LUNG 1.605697161.484435560.12767091Sensitive
NCIH322_LUNG 0.644645040.483234790.12767091Sensitive
NCIH441_LUNG 8.000000008.568929620.13216528Resistance
NCIH441_LUNG 8.000000006.271634580.13216528Resistance
NCIH441_LUNG 8.000000003.365048890.13216528Resistance
NCIH460_LUNG 8.00000000 NA0.10931684Resistance
NCIH460_LUNG 8.00000000 NA0.10931684Resistance
NCIH460_LUNG 8.00000000 NA0.10931684Resistance
NCIH520_LUNG 8.00000000 NA0.09788091Resistance
NCIH520_LUNG 8.000000008.207516250.09788091Resistance
NCIH520_LUNG 8.00000000 NA0.09788091Resistance
PC14_LUNG 2.149658923.442031620.13793057Sensitive
PC14_LUNG 0.226037950.212715160.13793057Sensitive
PC14_LUNG 0.064618560.013407790.13793057Sensitive
RERFLCAI_LUNG8.000000003.192855360.12752277Resistance
RERFLCAI_LUNG8.00000000 NA0.12752277Resistance
RERFLCAI_LUNG8.000000003.556686880.12752277Resistance
SKMES1_LUNG 1.476444121.548179390.13454178Sensitive
SKMES1_LUNG 1.429464585.732225420.13454178Sensitive
SKMES1_LUNG 0.965151311.085312370.13454178Sensitive

add chord plot¶

In [248]:
chord_data <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/res_orgin_state_transition_adata_meta.csv')
In [249]:
chord_data <- chord_data %>% dplyr::select(c('condition','metacell_label2'))
In [250]:
# Transform input data in a adjacency matrix
adjacencyData <- with(chord_data, table(condition, metacell_label2))
In [251]:
adjacencyData
                           metacell_label2
condition                   0 in 0 0 in 1 1 in 0 1 in 1 2 in 0 2 in 1 3 in 0
  Resistance cells in time0      0   2655      0    616      0    297      0
  Sensitive cells in time0     740      0    262      0     48      0     43
                           metacell_label2
condition                   3 in 1 4 in 1 5 in 1
  Resistance cells in time0    213    194     19
  Sensitive cells in time0       0      0      0
In [261]:
max(nchar(unlist(chord_data$metacell_label2)))
6
In [252]:
dim(adjacencyData)
  1. 2
  2. 10
In [270]:
grid.col <-c( brewer.pal(8, "Set2"),brewer.pal(4, "Set3"))
grid.col
  1. '#66C2A5'
  2. '#FC8D62'
  3. '#8DA0CB'
  4. '#E78AC3'
  5. '#A6D854'
  6. '#FFD92F'
  7. '#E5C494'
  8. '#B3B3B3'
  9. '#8DD3C7'
  10. '#FFFFB3'
  11. '#BEBADA'
  12. '#FB8072'
In [271]:
#https://www.jianshu.com/p/73c246b87d82
#circos.clear()#for Error: Since parameter has length larger than 1, it should have same length as the number of sectors.gap.degree
chordDiagram(
  adjacencyData,  grid.col = grid.col, 
  annotationTrack = c("grid"), 
  preAllocateTracks = list(
    track.height = max(strwidth(chord_data$metacell_label2))
  )
)
circos.track(
  track.index = 1, panel.fun = function(x, y) {
    sector.name = get.cell.meta.data("sector.index")
    if (nchar(sector.name) > max(nchar(unlist(chord_data$metacell_label2)))){
        message(sector.name)
        circos.text(
          CELL_META$xcenter, CELL_META$ylim[1], 
          CELL_META$sector.index,  facing = "inside", 
          niceFacing = TRUE, adj = c(0.5, 0)
        )
    }else{
    circos.text(
      CELL_META$xcenter, CELL_META$ylim[1], 
      CELL_META$sector.index,  facing = "clockwise", 
      niceFacing = TRUE, adj = c(0, 0.5)
    )
    }
  }, bg.border = NA
)
Resistance cells in time0

Sensitive cells in time0

No description has been provided for this image
In [273]:
pdf('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/res_orgin_state_transition_adata_meta.pdf')
chordDiagram(
  adjacencyData,  grid.col = grid.col, 
  annotationTrack = c("grid"), 
  preAllocateTracks = list(
    track.height = max(strwidth(chord_data$metacell_label2))
  )
)
circos.track(
  track.index = 1, panel.fun = function(x, y) {
    sector.name = get.cell.meta.data("sector.index")
    if (nchar(sector.name) > max(nchar(unlist(chord_data$metacell_label2)))){
        message(sector.name)
        circos.text(
          CELL_META$xcenter, CELL_META$ylim[1], 
          CELL_META$sector.index,  facing = "inside", 
          niceFacing = TRUE, adj = c(0.5, 0)
        )
    }else{
    circos.text(
      CELL_META$xcenter, CELL_META$ylim[1], 
      CELL_META$sector.index,  facing = "clockwise", 
      niceFacing = TRUE, adj = c(0, 0.5)
    )
    }
  }, bg.border = NA
)
dev.off()
Resistance cells in time0

Sensitive cells in time0

png: 2

survival¶

In [1255]:
probeMap <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUSC/gencode.v22.annotation.gene.probeMap')
GDC_phenotype <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUSC/TCGA-LUSC.GDC_phenotype.tsv')
htseq_counts <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUSC/TCGA-LUSC.htseq_counts.tsv')
survival <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUSC/TCGA-LUSC.survival.tsv')
In [1256]:
ann_htseq_counts <- merge(htseq_counts,probeMap,by.x = 'Ensembl_ID',by.y = 'id',all = F)
In [1257]:
ann_htseq_counts <- ann_htseq_counts %>% as.data.frame()
In [1258]:
ann_htseq_counts <- ann_htseq_counts[,c('gene',colnames(htseq_counts)[2:ncol(htseq_counts)])]
In [1259]:
#ann_htseq_counts <- aggregate(.~gene,mean,data=ann_htseq_counts)
In [1260]:
drivers$V1
  1. 'CD24'
  2. 'GTF2B'
  3. 'MSH6'
  4. 'TFDP1'
  5. 'MYO1B'
  6. 'TUBA1B'
  7. 'SLC1A5'
  8. 'TGM2'
  9. 'BARD1'
  10. 'TUBB4B'
  11. 'RRBP1'
  12. 'ANXA2'
  13. 'PLK2'
  14. 'PSMC4'
  15. 'DGKE'
  16. 'CEBPD'
  17. 'HSPA2'
  18. 'RAB31'
  19. 'TFAP2C'
  20. 'CD9'
  21. 'TPM1'
  22. 'LMO7'
  23. 'KLF5'
  24. 'TRIB1'
  25. 'HIST1H1C'
  26. 'DAAM1'
In [1261]:
score_df <- ann_htseq_counts %>% dplyr::filter(gene %in% drivers$V1) %>% merge(y=drivers,by.x = 'gene',by.y = 'V1',all = F)
In [1262]:
score <- apply(score_df[,2:ncol(ann_htseq_counts)],MARGIN  = 2,FUN = FUN5)
In [1263]:
score
TCGA-77-A5GA-01A
-0.00418401992486235
TCGA-58-8387-01A
-0.0122814135200296
TCGA-22-4599-01A
-0.00851164030087619
TCGA-77-7142-11A
0.000885729544116174
TCGA-NC-A5HJ-01A
-0.00399657731775459
TCGA-77-A5G6-01A
-0.00570628312440371
TCGA-O2-A52Q-01A
0.000922778346878163
TCGA-90-7769-01A
-0.00653773953040946
TCGA-56-8504-01A
-0.0045572034935121
TCGA-22-5472-11A
-0.000564361032854889
TCGA-77-A5GF-01A
-0.00457607295697404
TCGA-34-7107-11A
-0.00161032968585587
TCGA-66-2800-01A
-0.0090828247363286
TCGA-85-7697-01A
-0.00766154311461777
TCGA-85-8049-01A
-0.00727802827226768
TCGA-18-4086-01A
-0.00898380098921562
TCGA-63-A5MJ-01A
-0.00850169753919489
TCGA-98-8020-01A
-0.000483418416304344
TCGA-34-8454-11A
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-0.0080781621806133
TCGA-77-7463-01A
-0.000762903756072911
TCGA-37-4132-01A
-0.00881194763412812
TCGA-66-2778-01A
-0.0107801025326196
TCGA-37-5819-01A
-0.00446843683813398
TCGA-43-6773-01A
0.00565286035855402
TCGA-39-5040-11A
-0.00529658649060987
TCGA-NC-A5HI-01A
-0.00673249952228502
TCGA-56-7582-11A
-0.0113900432048767
TCGA-77-A5G3-01A
-0.00386249827250846
TCGA-21-1070-01A
-0.00884024257635302
TCGA-60-2715-01A
-0.00455173145977603
TCGA-60-2712-01A
-0.00164345738082029
TCGA-60-2707-01A
-0.0100152570757637
TCGA-63-5128-01A
-0.00125169916757649
TCGA-63-A5MH-01A
-0.00550024628204322
TCGA-39-5034-01A
-0.000388290400689806
TCGA-90-6837-11A
-0.00383371930726554
TCGA-60-2723-01A
-0.0187798672130036
TCGA-85-A4QQ-01A
0.000290623263558416
TCGA-18-3410-01A
-0.0105388453792112
TCGA-22-5485-01A
0.00217921554515213
TCGA-85-6798-01A
-0.00344760310706051
TCGA-66-2788-01A
-0.00885470633648343
TCGA-77-8131-01A
-0.00580554775322104
TCGA-46-3769-01A
-0.0054854786329607
TCGA-85-8048-01A
-0.0113964152082343
TCGA-22-5491-11A
-0.0133981809561774
TCGA-43-A475-01A
0.0018854999365596
TCGA-43-2576-01A
0.000126974916963481
TCGA-39-5030-01A
-0.00605780241274647
TCGA-85-A4JC-01A
-0.00443993627647879
TCGA-56-8628-01A
-0.00348046074398318
TCGA-66-2783-01A
0.00248063857024004
TCGA-56-8083-01A
-0.00280583383459152
TCGA-77-8143-01A
-0.00338663862356413
TCGA-94-A4VJ-01A
-0.00328511475193109
TCGA-66-2777-01A
-0.00149599742744529
TCGA-21-1080-01A
0.000635824876824649
TCGA-98-A53D-01A
-0.00602459628699935
TCGA-56-A4BX-01A
-0.00580821168049421
TCGA-77-7337-01A
-0.00901408429119115
TCGA-77-8145-01A
0.000541723994044196
In [1264]:
survival <- survival[match(names(score),survival$sample),]
survival$score <- score
In [1265]:
survival <- survival %>% na.omit()
In [1266]:
median(score)
-0.00382616957182098
In [1267]:
survival$group<- ifelse((survival$score < median(score)), 1,2)
In [1268]:
survival
A data.table: 542 × 6
sampleOS_PATIENTOS.timescoregroup
<chr><int><chr><int><dbl><dbl>
TCGA-77-A5GA-01A0TCGA-77-A5GA1280-4.184020e-031
TCGA-58-8387-01A1TCGA-58-8387 403-1.228141e-021
TCGA-22-4599-01A1TCGA-22-45991161-8.511640e-031
TCGA-77-7142-11A0TCGA-77-71422227 8.857295e-042
TCGA-NC-A5HJ-01A1TCGA-NC-A5HJ 418-3.996577e-031
TCGA-77-A5G6-01A1TCGA-77-A5G6 678-5.706283e-031
TCGA-O2-A52Q-01A1TCGA-O2-A52Q 113 9.227783e-042
TCGA-90-7769-01A0TCGA-90-7769 358-6.537740e-031
TCGA-56-8504-01A0TCGA-56-8504 510-4.557203e-031
TCGA-22-5472-11A1TCGA-22-54721975-5.643610e-042
TCGA-77-A5GF-01A1TCGA-77-A5GF 840-4.576073e-031
TCGA-34-7107-11A1TCGA-34-7107 34-1.610330e-032
TCGA-66-2800-01A0TCGA-66-28001492-9.082825e-031
TCGA-85-7697-01A0TCGA-85-76971063-7.661543e-031
TCGA-85-8049-01A0TCGA-85-8049 579-7.278028e-031
TCGA-18-4086-01A1TCGA-18-4086 85-8.983801e-031
TCGA-63-A5MJ-01A0TCGA-63-A5MJ1824-8.501698e-031
TCGA-98-8020-01A1TCGA-98-8020 84-4.834184e-042
TCGA-34-8454-11A0TCGA-34-84541180-1.785622e-032
TCGA-90-6837-01A0TCGA-90-6837 758-1.166233e-021
TCGA-66-2785-01A0TCGA-66-2785 60-8.017311e-031
TCGA-68-8250-01A0TCGA-68-8250 244-1.724329e-032
TCGA-56-A5DR-01A0TCGA-56-A5DR 4-3.930295e-031
TCGA-33-4589-01A1TCGA-33-4589 47-1.449521e-021
TCGA-NK-A5D1-01A0TCGA-NK-A5D1 511-1.703163e-032
TCGA-39-5022-01A1TCGA-39-50221679 9.089691e-062
TCGA-L3-A4E7-01A0TCGA-L3-A4E7 392-3.770393e-032
TCGA-39-5037-01A0TCGA-39-50371690-7.669179e-031
TCGA-43-6143-11A0TCGA-43-6143 699-2.808283e-032
TCGA-77-7338-11A1TCGA-77-7338 5 2.279184e-032
⋮⋮⋮⋮⋮⋮
TCGA-66-2767-01A0TCGA-66-2767 61-0.01001525711
TCGA-77-7142-01A0TCGA-77-71422227-0.00125169922
TCGA-22-5477-01A1TCGA-22-54771346-0.00550024631
TCGA-56-7222-11A1TCGA-56-7222 562-0.00038829042
TCGA-85-7698-01A0TCGA-85-7698 952-0.00383371931
TCGA-66-2766-01A0TCGA-66-2766 31-0.01877986721
TCGA-56-7731-11A1TCGA-56-7731 3 0.00029062332
TCGA-NC-A5HL-01A1TCGA-NC-A5HL 88-0.01053884541
TCGA-34-5239-01A0TCGA-34-52391834 0.00217921552
TCGA-56-8308-01A0TCGA-56-8308 517-0.00344760312
TCGA-L3-A524-01A1TCGA-L3-A524 490-0.00885470631
TCGA-37-3789-01A0TCGA-37-3789 13-0.00580554781
TCGA-77-8007-11A1TCGA-77-8007 198-0.00548547861
TCGA-77-8128-01A1TCGA-77-81281150-0.01139641521
TCGA-60-2713-01A0TCGA-60-27131731-0.01339818101
TCGA-77-8146-01A0TCGA-77-81463189 0.00188549992
TCGA-NC-A5HM-01A0TCGA-NC-A5HM1212 0.00012697492
TCGA-92-7341-01A0TCGA-92-7341 106-0.00605780241
TCGA-94-8035-01A0TCGA-94-8035 122-0.00443993631
TCGA-60-2722-01A0TCGA-60-2722 908-0.00348046072
TCGA-51-4079-11A1TCGA-51-4079 12 0.00248063862
TCGA-77-7465-01A0TCGA-77-7465 990-0.00280583382
TCGA-63-A5MG-01A0TCGA-63-A5MG2148-0.00338663862
TCGA-56-7579-01A1TCGA-56-7579 951-0.00328511482
TCGA-77-8148-01A0TCGA-77-81482023-0.00149599742
TCGA-39-5039-01A1TCGA-39-5039 544 0.00063582492
TCGA-43-8118-01A1TCGA-43-8118 89-0.00602459631
TCGA-43-6647-01A0TCGA-43-6647 757-0.00580821171
TCGA-18-3406-01A1TCGA-18-3406 371-0.00901408431
TCGA-56-7730-11A1TCGA-56-7730 198 0.00054172402
In [1269]:
library(survival)
In [1270]:
fit.surv <-Surv(survival$OS.time,survival$OS)
In [1271]:
km<-survfit(fit.surv~1,data = survival)
km_2<- survfit(fit.surv~group,data=survival)
In [1272]:
library(survminer)
Loading required package: ggpubr


Attaching package: ‘survminer’


The following object is masked from ‘package:survival’:

    myeloma


In [1273]:
ggsurvplot (km)
No description has been provided for this image
In [1274]:
ggsurvplot(km_2)
No description has been provided for this image
In [1275]:
p <- ggsurvplot(km_2, main = "Survival curve",
           conf.int = TRUE,# 可信区间
           palette = "npg",# 支持ggsci配色,自定义颜色,brewer palettes中的配色,等
           ggtheme = theme_classic(), # 支持ggplot2及其扩展包的主题
           legend.title = "Score",#改变图例名称
           legend.labs = c("Low", "High"),
           risk.table = TRUE,        # 增加risk table
           risk.table.title = "Risk set sizes",
           ncensor.plot = TRUE, #增加删失事件表
           surv.median.line = "hv",
           pval=TRUE,  #添加P值
           log.rank.weights = "1", #pval.method
)
p
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
No description has been provided for this image
In [1276]:
#https://github.com/kassambara/survminer/issues/152

# add method to grid.draw
grid.draw.ggsurvplot <- function(x){
  survminer:::print.ggsurvplot(x, newpage = FALSE)
}
# Remember to pass object `p`.
ggsave(
  filename =paste0('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/survplot_TCGA_LUSC_byScore.pdf'),
  plot = p,
  device = 'pdf',
  #path = 'data/output',
  width = 8,
  height = 9
)
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
In [1277]:
probeMap <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUNG/probeMap_hugo_gencode_good_hg19_V24lift37_probemap')
GDC_phenotype <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUNG/TCGA.LUNG.sampleMap_LUNG_clinicalMatrix')
htseq_counts <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUNG/TCGA.LUNG.sampleMap_HiSeqV2_PANCAN')
survival <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUNG/survival_LUNG_survival.txt')
In [1278]:
htseq_counts
A data.table: 20530 × 1130
sampleTCGA-NJ-A4YP-01TCGA-18-3417-01TCGA-22-4613-01TCGA-90-7769-01TCGA-62-8397-01TCGA-77-A5G1-01TCGA-86-8278-01TCGA-77-A5G3-01TCGA-05-4430-01⋯TCGA-63-A5MY-01TCGA-50-6593-01TCGA-33-AASL-01TCGA-85-A512-01TCGA-85-8354-01TCGA-O2-A5IB-01TCGA-67-3771-01TCGA-77-7335-01TCGA-55-8302-01TCGA-56-7731-11
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
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REM1 0.70235434 0.01625434-0.61824566-1.370945657-1.48874566 0.012254343-2.13474566-1.98774566 1.31905434⋯-1.3584457 0.36575434 1.58005434-0.74814566-1.519245657-2.194045657-0.3241457 1.01505434-0.217745657 0.19415434
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C16orf13 0.49771071 0.53551071-0.58188929-0.112389286 0.22271071-1.224589286-0.30338929-0.41028929 0.46571071⋯-0.6639893 0.48841071 0.30781071 0.32871071-0.134489286-1.419589286-1.3920893-0.04558929 0.641410714-0.33348929
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ATRX -0.15600253-1.50410253-0.99640253-1.523502528-0.07540253-0.048702528 0.81019747 0.67369747-0.42180253⋯-0.3381025-0.30870253-0.71980253-0.21970253-0.300802528 0.962397472-0.4801025-0.09250253-0.619302528-0.16670253
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ASS1 2.31555096 0.55765096-0.36884904 0.734950956 1.69325096-0.046249044 1.67695096 0.53495096 1.93435096⋯-3.0288490-0.20114904 2.22865096 1.25725096-0.377749044-1.331849044 0.9728510-0.84344904-0.273349044 0.88875096
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DISC1 0.73228609-1.68861391 0.45078609-1.374913906-0.16911391-1.004313906-0.12131391-0.57021391-0.36191391⋯-0.8773139-0.24531391-1.49991391 1.30418609-1.538513906-0.623313906 0.5632861-0.03331391-0.659213906-0.69791391
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RPL37 0.75441135 1.63201135 1.00481135 1.682411354 0.50691135 0.417711354-0.22568865 0.50181135 1.00511135⋯ 2.0032114 1.18501135 0.89991135 0.14291135 0.501111354-0.732088646-0.8980886 0.47681135 0.016511354-0.35328865
SPR 0.85050614 1.74890614 0.87510614 0.519106141 0.71690614 0.005206141 0.32550614-0.05889386 0.28350614⋯ 0.3018061 0.42510614 1.52840614 1.34400614 0.556906141-2.203593859 0.1602061 0.60540614 0.575906141-0.49089386
ZNF700 0.20937597-1.81582403 0.41927597-0.373024034-0.68872403-0.104324034-0.07102403 0.74767597-0.36182403⋯ 0.7786760-0.74442403 1.51807597 0.55597597 1.507575966 0.768675966 0.8267760-1.01532403-0.149624034-0.84362403
ZNF707 -0.11726627-1.70956627 0.30083373 0.139233734-0.02576627 0.269833734-0.03936627 0.32633373-0.90756627⋯ 0.9233337 0.36203373 1.36013373 0.38423373 0.004133734 0.686733734 0.2414337-0.94576627 0.936633734-0.54406627
CAMK4 -0.33729762-0.23089762 0.15240238 0.148602380-0.51469762 1.315802380 0.11300238 0.30270238-0.39799762⋯-2.4900976-0.42919762 0.46130238 0.07560238 1.386702380 1.723102380-0.8621976 0.98030238-1.421497620 0.03300238
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋱⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
C8orf83 0.03290948 0.32060948-0.44609052-2.11409052-0.09959052-0.143490519 0.2177095-1.48989052 0.57100948⋯-2.24969052 0.25460948-0.14239052-0.40879052-2.75329052-0.57079052 0.48360948-0.06379052 1.255009481-0.45389052
C1orf192 0.10989140 1.28659140-0.66340860-0.76880860-0.70410860-1.201008599 1.0494914-1.48060860 3.69969140⋯-0.96190860 2.38939140-3.50440860-0.81860860-1.09750860 0.46509140 0.12489140-0.72870860-1.212108599 2.67099140
PLAA 0.04717590 0.46387590-0.40722410 0.60587590 0.19177590 0.112775895-0.0782241 0.41727590-0.05912410⋯ 1.56607590-0.11452410 0.10247590-0.53052410-4.50632410 1.17717590 0.41177590 0.01827590-0.537924105 0.39647590
C8orf80 0.66281659 0.08551659 2.44371659-1.45958341 0.55581659 0.310516590 2.1343166-0.65238341 0.13601659⋯-1.03118341 0.22771659-0.54828341-0.68908341-1.22758341-1.39298341 2.87801659 1.71711659 0.678916590-1.12108341
C8orf86 -0.45138713-0.45138713-0.45138713-0.45138713-0.45138713-0.451387132-0.4513871 0.43861287-0.45138713⋯-0.45138713-0.45138713-0.45138713-0.45138713-0.45138713-0.45138713-0.45138713-0.45138713-0.451387132-0.45138713
C8orf85 -1.77766307-1.52886307-0.40506307-0.66826307 2.56753693 0.735536925-1.3063631-0.54136307 1.00973693⋯-2.30586307 2.02763693 1.17093693-2.25686307-1.04246307 3.68983693 1.06223693-1.40146307 0.468636925 0.18613693
C8orf84 -1.56227245-4.56197245 1.23302755-2.97487245-1.44027245-2.010172454-0.9391725 0.37072755 0.10062755⋯-2.30087245-0.36257245 2.93382755 1.18082755-1.53587245-1.16357245-0.98357245 0.34992755-1.822772454 2.02962755
NFIC -0.14410189-0.64260189-0.24730189-0.01530189 0.23749811-0.577301888 1.0277981 0.24389811 0.61819811⋯-2.00590189 0.61279811-1.50940189-0.52790189 0.07359811 1.48449811-0.45400189-0.34500189 0.001798112 1.02519811
NFIB 0.31388817-1.20851183-0.84801183-1.53141183-1.70541183-0.443911830 1.3089882-0.03711183 0.14648817⋯-1.16131183 0.29248817-1.98031183 0.02148817 0.36468817 2.58358817 0.29758817 0.46798817-0.800411830 0.47078817
H1FOO -0.06373190-0.06373190-0.06373190-0.06373190-0.06373190-0.063731900-0.0637319-0.06373190-0.06373190⋯-0.06373190-0.06373190-0.06373190-0.06373190-0.06373190-0.06373190-0.06373190-0.06373190-0.063731900-0.06373190
NFIX -1.10609312-1.12039312-0.39449312-1.50569312 0.73430688-1.586693117-0.2269931-0.07199312-0.52069312⋯-1.64279312-0.68509312-1.20429312-0.70819312 0.71170688 0.63590688-1.08279312-0.19549312-1.759293117 0.13030688
PLEKHG6 -2.29898743 4.02121257 1.99571257 3.79631257 2.02511257 3.689112571 0.8403126 2.47781257 1.10951257⋯ 2.32401257 1.80701257 3.38491257 4.06351257 2.29741257 0.73881257 1.63491257 2.84901257 2.882812571 0.13031257
PLEKHG7 2.03181071-1.42478929-0.65978929-1.75268929 2.45481071 0.210810714 3.1658107-0.69778929 2.78931071⋯-0.15948929 3.48171071-1.36658929-1.21358929-1.50408929 4.29511071 4.97391071-0.23408929 1.697010714 1.50601071
PLEKHG4 -1.46484297 0.76185703 2.12695703 1.42405703 1.46435703-0.468342973 1.4031570 2.59915703 0.11375703⋯ 1.11765703 0.41355703 1.38405703 2.41445703 3.17285703 3.01505703 2.00355703-0.23194297 1.136257027-1.16154297
PLEKHG5 0.29471306-1.39268694 0.18811306 1.20341306 0.56571306 1.215213055 0.6953131 1.19931306-0.43438694⋯ 0.99411306 0.30401306 1.31551306 1.77041306 1.22431306 2.67201306 1.70751306-0.26218694 1.027013055-0.82078694
SLC7A14 -1.99013046-1.99013046-1.99013046-1.54993046-1.99013046-1.466930464-1.9901305-1.47753046-1.99013046⋯-1.99013046-1.99013046-1.99013046-0.99503046-1.99013046-1.64963046 2.32406954-0.13963046-1.990130464-1.99013046
SELE 1.82569117 0.48469117 0.92539117 0.75329117-2.64330883 2.244191174-0.8919088-1.95320883-0.65030883⋯-2.08000883-0.33430883-0.52800883 1.02799117-1.26890883-3.42470883-1.55690883 0.27779117-2.629808826 5.41289117
SLC7A10 2.27721425-2.09078575-2.09078575-1.29888575 2.44821425-0.868485751 0.7149142 0.31531425 6.26281425⋯ 0.54961425 2.03461425 0.18661425-2.09078575-0.73828575 0.38101425 2.55911425-2.09078575-1.454985751 0.43871425
PLA2G2C -0.08668212-0.08668212-0.08668212-0.08668212-0.08668212-0.086682123 0.5160179-0.08668212 1.09971788⋯-0.08668212-0.08668212-0.08668212-0.08668212-0.08668212-0.08668212-0.08668212-0.08668212 0.549117877-0.08668212
METTL11A 0.56927707 0.34377707 0.59407707 0.55277707 0.19457707-0.334222926 0.2088771 0.51897707 0.55757707⋯-0.40062293-0.26792293 0.37937707 0.74207707 0.67257707-1.71202293 0.15797707-0.52962293 0.481377074 0.11737707
TULP2 0.06852169-0.74887831-0.74887831 0.02802169-0.74887831 0.157421693-0.4162783 0.14112169 0.43752169⋯-0.74887831 0.79922169-0.74887831-0.74887831 0.27662169-0.40837831 3.24962169-0.74887831-0.748878307-0.74887831
NPY5R -1.58711704-1.58711704-1.58711704-1.58711704-1.58711704-1.587117043-1.5871170-1.58711704 0.10568296⋯-1.58711704-0.87221704-0.89141704-1.58711704-1.58711704-1.24661704-0.86331704-1.58711704-1.587117043-0.39601704
GNGT2 0.93176654 0.30496654 0.77966654-2.52823346 0.91336654 0.382066539 0.5875665-1.15863346 1.30426654⋯-2.55633346 1.10506654-0.79953346-0.23603346-0.78413346-1.94053346 0.41606654 2.16036654 1.078866539 2.03046654
GNGT1 -0.46398982 2.60701018 1.15821018 2.07981018 0.01721018 1.361410183-1.2813898 0.99351018-0.56948982⋯ 2.61571018-1.28138982 2.70701018 1.41071018 1.46201018-1.28138982-1.28138982 1.19391018-1.281389817-1.28138982
TULP3 -0.00267684 1.30262316 0.12402316-0.43147684-0.33867684 0.588023160 0.4320232 0.58352316 0.02512316⋯ 0.22692316-0.31237684 0.11782316 1.65372316 0.76502316 1.28172316-0.51127684 0.19702316 0.280623160-0.79007684
PTRF 0.74571421 1.02501421-0.39668579 0.09471421-0.00518579 1.569614210 0.1083142 1.26991421 0.88161421⋯-0.22258579 0.33231421 0.77471421 0.43441421 1.24691421-2.42558579-0.86608579 1.55011421 0.634014210 2.46851421
BCL6B 1.46177337-0.38102663-0.78132663-2.26782663-0.73522663-0.409226625-0.2908266-0.54212663 1.71117337⋯-1.71472663 0.70387337-2.30672663-0.49142663-0.07002663-0.73442663 0.53747337 0.29477337-0.909526625 2.66797337
GSTK1 -0.42619462 0.83930538-0.73249462-1.13709462 0.20410538-0.003394623-1.3093946-1.32499462-0.50409462⋯ 0.25160538 0.30260538 0.40620538 0.31830538-0.62069462-1.92679462-1.50859462 0.36900538-0.114894623-0.26469462
SELP -0.64503328-1.08503328 1.73386672-1.21023328 1.33656672 1.761466719 0.1984667-1.78443328 2.47906672⋯-0.01853328 1.73806672-2.34673328-1.01143328-1.39033328-2.12163328 0.35666672 1.90926672 0.848866719 3.93086672
SELS 0.40018760 0.53318760 0.62448760 0.15368760-0.59821240 0.738687600 0.4574876 0.88168760 0.31168760⋯ 0.51108760-0.37241240 0.87938760 0.39228760 0.91888760-0.63741240 0.78618760 0.11768760 0.195587600 0.84258760
In [1279]:
drivers$V1
  1. 'CD24'
  2. 'GTF2B'
  3. 'MSH6'
  4. 'TFDP1'
  5. 'MYO1B'
  6. 'TUBA1B'
  7. 'SLC1A5'
  8. 'TGM2'
  9. 'BARD1'
  10. 'TUBB4B'
  11. 'RRBP1'
  12. 'ANXA2'
  13. 'PLK2'
  14. 'PSMC4'
  15. 'DGKE'
  16. 'CEBPD'
  17. 'HSPA2'
  18. 'RAB31'
  19. 'TFAP2C'
  20. 'CD9'
  21. 'TPM1'
  22. 'LMO7'
  23. 'KLF5'
  24. 'TRIB1'
  25. 'HIST1H1C'
  26. 'DAAM1'
In [1280]:
score_df <- htseq_counts %>% dplyr::filter(gene %in% drivers$V1) %>% merge(y=drivers,by.x = 'sample',by.y = 'V1',all = F)
score <- apply(score_df[,2:ncol(htseq_counts)],MARGIN  = 2,FUN = FUN1)
In [1281]:
score
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TCGA-18-3417-01
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TCGA-22-4613-01
0.014850111080764
TCGA-90-7769-01
0.0244142263885895
TCGA-62-8397-01
-0.00532703515339611
TCGA-77-A5G1-01
0.0278548672008436
TCGA-86-8278-01
0.00957823410660976
TCGA-77-A5G3-01
0.0218453711235411
TCGA-05-4430-01
0.0150223381737708
TCGA-44-6778-01
0.0131329977617059
TCGA-49-AARQ-01
-0.00597550512272976
TCGA-66-2766-01
0.0232599837646498
TCGA-MP-A4TK-01
0.0106267320904438
TCGA-37-4135-01
0.0105433143533428
TCGA-55-6975-01
0.0215167338612357
TCGA-56-8201-01
0.0141304396534083
TCGA-56-7582-11
0.00935247924648831
TCGA-43-7656-01
0.0140372337500545
TCGA-85-8481-01
0.0331640891138915
TCGA-56-A4ZK-01
0.0208564428778199
TCGA-56-8309-11
0.0112404426340728
TCGA-22-1011-01
0.0247210733023066
TCGA-78-7160-01
0.0063607607914982
TCGA-77-8156-01
0.0118523309144465
TCGA-78-8655-01
0.00528674456618286
TCGA-43-A475-01
0.00809404849165136
TCGA-69-8255-01
-0.00358904061479728
TCGA-67-3773-01
0.00309979429854287
TCGA-37-3789-01
0.0256030702440885
TCGA-97-8552-01
0.0048810960547126
TCGA-53-A4EZ-01
-0.000228949201570441
TCGA-22-4596-01
0.0121188452077081
TCGA-58-A46N-01
0.0144254689521586
TCGA-63-A5MW-01
0.014414123117864
TCGA-85-8479-01
0.0218909073830893
TCGA-68-8251-01
0.0125064031819798
TCGA-34-7107-01
0.0103338281302336
TCGA-37-4141-01
0.015216850829504
TCGA-55-7910-01
0.00961496170356521
TCGA-62-A46U-01
0.00643075255638448
TCGA-MP-A4T8-01
0.00602379810003126
TCGA-97-8179-01
0.00435866728474908
TCGA-39-5040-01
0.0308587415482346
TCGA-43-2578-01
0.0178760021748852
TCGA-49-4510-01
0.00633747586776478
TCGA-63-A5M9-01
0.0156123284063398
TCGA-92-8063-01
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TCGA-78-7162-01
0.00632412247872079
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0.0221400445501076
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0.0285099505355347
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0.0199409979957098
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0.0117652799898072
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0.0103021737699118
TCGA-46-3767-01
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0.0167734180155275
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0.00430583684037739
TCGA-50-5930-11
0.00583475547357668
TCGA-51-4080-01
0.0128382583992653
TCGA-33-6737-01
0.0212998144221687
TCGA-55-8090-01
0.0168603959757005
TCGA-56-7223-01
0.00725754859648304
TCGA-55-8091-01
0.00432638003368033
TCGA-38-4626-11
0.0104384069671753
TCGA-39-5011-01
0.0117565496807995
TCGA-MP-A4SW-01
0.00531721906800572
TCGA-78-7159-01
0.00749373286191961
TCGA-39-5037-01
0.00594061684490016
TCGA-66-2777-01
0.0190896866616876
TCGA-67-6216-01
0.0044402169715805
TCGA-21-5783-01
0.0137545620646357
TCGA-38-4627-01
0.0145840959528446
TCGA-38-4630-01
0.00316913768275158
TCGA-J2-8194-01
0.00536636146458205
TCGA-55-6968-01
0.0194043639451136
TCGA-85-7950-01
0.0161260093062436
TCGA-77-8153-01
0.014207715010054
TCGA-L3-A4E7-01
0.0149129201700443
TCGA-60-2724-01
0.019094538684193
TCGA-44-2668-01
0.0167618808092033
TCGA-90-A59Q-01
0.00576129607181202
TCGA-55-A57B-01
0.0128361526695569
TCGA-55-8614-01
0.0218929116932624
TCGA-55-8507-01
0.00822209780712547
TCGA-94-7557-01
0.0152383829262764
TCGA-96-7545-01
0.0193770084702441
TCGA-85-6798-01
0.00911283534722905
TCGA-22-5491-11
0.0093829552783429
TCGA-75-7027-01
0.0129802390439442
TCGA-44-A47B-01
-0.00346537918973065
TCGA-98-7454-01
0.0159687302438629
TCGA-38-4625-01
0.0071126431411916
TCGA-44-A4SU-01
⋯
TCGA-L9-A444-01
0.015875703635364
TCGA-66-2771-01
0.0135727729847341
TCGA-56-8083-01
0.00601409286897683
TCGA-85-7696-01
0.0120104545024926
TCGA-77-A5G6-01
0.0153409899814711
TCGA-90-A4ED-01
0.00440587927330999
TCGA-56-A4BX-01
0.0135911811676292
TCGA-55-6978-11
0.0109517872195727
TCGA-NC-A5HG-01
0.0145979447869337
TCGA-50-5072-01
0.0146481703911385
TCGA-37-5819-01
0.0113603776868481
TCGA-55-8512-01
0.013852179477484
TCGA-66-2727-01
0.0132911888354344
TCGA-77-6843-01
0.0184998484871167
TCGA-77-8130-01
0.0106502755295212
TCGA-86-8672-01
0.0112740469706127
TCGA-34-5928-01
0.0100392177422081
TCGA-44-A47G-01
0.00697207930847491
TCGA-22-4594-01
0.00780127990718505
TCGA-69-8253-01
0.0149533397222255
TCGA-55-6980-11
0.0102775050411052
TCGA-91-6849-01
0.020722369998935
TCGA-44-6146-11
0.0118616849404947
TCGA-22-A5C4-01
0.0131619448952235
TCGA-33-A5GW-01
0.0193720284219137
TCGA-94-8035-01
0.01483019179463
TCGA-94-A5I4-01
0.0209826322119348
TCGA-94-7943-01
0.0206240774406033
TCGA-49-4512-01
0.0239691244195722
TCGA-86-8279-01
0.00471657619849237
TCGA-85-A53L-01
0.00744155737978851
TCGA-63-5128-01
0.00374386699405458
TCGA-63-A5MU-01
0.0214425671079832
TCGA-L9-A743-01
0.0136238720409464
TCGA-37-4129-01
0.00548427132945583
TCGA-62-A46P-01
0.00593341343348432
TCGA-98-A53C-01
0.010909131854187
TCGA-62-A46R-01
0.0126315084017617
TCGA-55-8619-01
0.00945588111687359
TCGA-56-A4ZJ-01
0.0163713090935645
TCGA-44-2665-01
-0.00237241048911074
TCGA-55-8096-01
0.0102304173347155
TCGA-50-6592-01
0.00265870827882859
TCGA-55-A494-01
0.0172339720558509
TCGA-22-0940-01
0.00939576694682473
TCGA-69-8254-01
0.00940527328960211
TCGA-77-8145-01
0.0185120768869427
TCGA-53-7624-01
0.0123454774531288
TCGA-55-6984-11
0.00632841423287126
TCGA-O2-A52W-01
0.0201195989851954
TCGA-43-6771-11
0.00587594159066502
TCGA-75-5125-01
0.0153071070447202
TCGA-63-A5MV-01
0.01438777599202
TCGA-34-5231-01
0.0135987494522933
TCGA-33-AASD-01
0.0152507668886747
TCGA-77-8140-01
0.00526740115984008
TCGA-39-5019-01
0.0177514276413793
TCGA-22-5478-01
0.0211832093657787
TCGA-86-8054-01
0.00459327704611518
TCGA-90-7964-01
0.00760327998237504
TCGA-L4-A4E5-01
0.0139736580992043
TCGA-68-8250-01
0.0184515633628014
TCGA-44-6775-01
0.00246737879288246
TCGA-55-7913-01
0.00879364186858593
TCGA-56-8504-01
0.0160500207531915
TCGA-NC-A5HP-01
0.0127518812945452
TCGA-55-7911-01
0.0193754835833314
TCGA-33-4566-01
0.0107989882896165
TCGA-58-8393-01
0.00430723574423263
TCGA-63-A5MS-01
0.0210253285882
TCGA-MP-A4T9-01
0.00946929525973511
TCGA-55-6712-01
0.0136690623101112
TCGA-66-2780-01
0.00619329843500031
TCGA-78-8640-01
0.0152624657128189
TCGA-86-8585-01
0.0163648110837476
TCGA-21-1075-01
0.0132736212776257
TCGA-56-7823-01
0.00299662874245165
TCGA-91-6840-01
0.01890350612468
TCGA-63-A5MP-01
0.0180424462480948
TCGA-69-7979-01
0.0126145856692243
TCGA-85-8072-01
0.0219804959010989
TCGA-21-1078-01
-0.00730847540700536
TCGA-55-A4DG-01
-0.00433534175930682
TCGA-55-7816-01
0.0168154837414902
TCGA-77-A5GH-01
0.0171353803489168
TCGA-60-2714-01
0.0123101925092093
TCGA-34-2600-01
0.00937294586514041
TCGA-55-7815-01
0.0135721742138672
TCGA-33-4533-01
0.0257184655343895
TCGA-85-A510-01
0.0224561695834373
TCGA-MN-A4N4-01
0.0197451475981396
TCGA-52-7622-01
0.0178912055668444
TCGA-77-7337-01
0.0097764974469333
TCGA-37-A5EN-01
0.0186887121865384
TCGA-77-8148-01
0.0210044140726446
TCGA-18-5592-01
0.0133625521842808
TCGA-77-7335-11
0.0114322897052353
TCGA-97-A4M0-01
0.0259809027967772
TCGA-55-6969-11
0.0140980761381052
TCGA-92-7341-01
0.0207143739689919
TCGA-44-2661-01
0.0138060572482492
TCGA-34-A5IX-01
0.0162963743884066
TCGA-56-8623-01
-0.000635657590659697
TCGA-63-A5MI-01
0.0155316218679957
TCGA-55-8203-01
0.0184083627597658
TCGA-66-2791-01
0.000396297662592241
TCGA-21-1071-01
0.0177739229227877
TCGA-60-2711-01
0.0166433786793905
TCGA-49-6743-11
-0.0130493768855369
TCGA-37-4133-01
0.0118094501089099
TCGA-34-8454-01
0.00561590065814699
TCGA-55-6985-01
0.0147226845210072
TCGA-77-8146-01
0.00689151481169684
TCGA-L9-A5IP-01
0.0100857969593694
TCGA-56-7582-01
0.013116887761903
TCGA-75-5126-01
0.00865807201466872
TCGA-18-3406-01
0.0261926966585413
TCGA-56-8309-01
0.00848948434223168
TCGA-73-4662-01
0.017708579340981
TCGA-44-2655-01
0.000426282476396538
TCGA-21-1080-01
0.00309150532565452
TCGA-93-7347-01
0.0221064562030549
TCGA-MP-A4TF-01
0.000182611979200796
TCGA-78-7535-01
0.0213084704843989
TCGA-60-2721-01
0.0123227609419139
TCGA-60-2716-01
0.0147134505496912
TCGA-62-A471-01
0.0159358077667271
TCGA-49-4490-01
0.0118603519745776
TCGA-66-2737-01
0.0110350230379942
TCGA-63-7021-01
0.0161636099488254
TCGA-86-8671-01
0.0226070265039971
TCGA-73-4666-01
0.0258640619397138
TCGA-85-8352-01
0.0035558164835295
TCGA-85-7698-01
0.00506913723910223
TCGA-38-4628-01
0.0105125920802997
TCGA-77-8139-01
0.0135044278777492
TCGA-34-7107-11
0.0211181229869924
TCGA-64-5775-01
0.00456957378289423
TCGA-86-7953-01
0.0160502204907882
TCGA-38-6178-01
0.0120332634148462
TCGA-44-7672-01
0.0122691819256837
TCGA-98-8020-01
0.00862190988930192
TCGA-85-8052-01
0.00648260142222829
TCGA-55-6979-01
0.00519721766878458
TCGA-55-7573-01
-0.00422926604733469
TCGA-55-8097-01
0.0181102090950499
TCGA-43-3920-01
0.0071846147289651
TCGA-77-7142-01
-0.000635380376002356
TCGA-97-7546-01
0.014645434794326
TCGA-NC-A5HK-01
0.000661024056522213
TCGA-05-5425-01
0.0184444447318634
TCGA-55-6970-01
0.0147516656561938
TCGA-43-7658-11
0.0211683245840567
TCGA-60-2719-01
0.0178993146048817
TCGA-50-5946-02
0.00777548825191501
TCGA-58-8387-01
0.0115894698820752
TCGA-55-1596-01
0.0218651109808439
TCGA-43-7658-01
0.0107111842306313
TCGA-78-7148-01
0.00983357311964689
TCGA-96-A4JK-01
0.00629485857615775
TCGA-50-5066-02
0.0108438577406798
TCGA-50-5066-01
0.00605670811236899
TCGA-43-6143-01
0.0075376116855206
TCGA-56-8305-01
0.0165917244703022
TCGA-55-8092-01
0.0180227324534112
TCGA-50-8459-01
0.0196315039460845
TCGA-90-6837-11
0.024714214274047
TCGA-75-6207-01
0.00409518179896989
TCGA-44-2665-11
0.0221599499068418
TCGA-66-2769-01
0.0171233531584745
TCGA-97-7938-01
0.00232362974349687
TCGA-22-1000-01
0.0123949290130394
TCGA-18-3412-01
0.0188560902448004
TCGA-66-2759-01
0.0185974097104029
TCGA-49-AARE-01
0.00602470048583457
TCGA-NJ-A7XG-01
0.0119656199366797
TCGA-66-2794-01
0.0149087396324592
TCGA-21-1083-01
-0.00192173740460946
TCGA-60-2695-01
0.00615355005476825
TCGA-50-5055-01
0.0135507877736679
TCGA-85-6560-01
0.0192509798623795
TCGA-98-8022-01
0.0073124514277153
TCGA-91-8496-01
0.00836076355919284
TCGA-43-5668-01
0.0059065595378326
TCGA-21-5786-01
0.0145076564787722
TCGA-05-4418-01
0.0198227123066194
TCGA-77-8007-11
-0.00352141114333758
TCGA-34-8456-01
0.0222366525480339
TCGA-69-7764-01
0.01457508445816
TCGA-55-8620-01
0.004460156587941
TCGA-95-7043-01
0.0066230619634217
TCGA-66-2755-01
0.00968610337812382
TCGA-18-4083-01
0.0174891777930031
TCGA-86-7701-01
0.0101575235204199
TCGA-56-7222-01
0.0134943831963811
TCGA-52-7812-01
0.0124592501876909
TCGA-94-8490-01
0.0199277866851551
TCGA-73-4676-01
0.0157407043085665
TCGA-44-6777-01
0.0160357368490573
TCGA-51-4081-01
0.013989310155162
In [1282]:
survival
A data.table: 1145 × 11
sample_PATIENTOSOS.timeDSSDSS.timeDFIDFI.timePFIPFI.timeRedaction
<chr><chr><int><int><int><int><int><int><int><int><lgl>
TCGA-05-4244-01TCGA-05-42440 0 0 0NA NA0 0NA
TCGA-05-4249-01TCGA-05-424901523 01523NA NA01523NA
TCGA-05-4250-01TCGA-05-42501 121NA 121NA NA0 121NA
TCGA-05-4382-01TCGA-05-43820 607 0 607 1 3341 334NA
TCGA-05-4384-01TCGA-05-43840 426 0 426NA NA1 183NA
TCGA-05-4389-01TCGA-05-438901369 01369NA NA01369NA
TCGA-05-4390-01TCGA-05-439001126 01126NA NA1 395NA
TCGA-05-4395-01TCGA-05-43951 0 0 0NA NA0 0NA
TCGA-05-4396-01TCGA-05-43961 303NA 303NA NA0 303NA
TCGA-05-4397-01TCGA-05-43971 731NA 731NA NA0 731NA
TCGA-05-4398-01TCGA-05-439801431 01431 0143101431NA
TCGA-05-4402-01TCGA-05-44021 244 0 244NA NA0 244NA
TCGA-05-4403-01TCGA-05-44030 578 0 578NA NA0 578NA
TCGA-05-4405-01TCGA-05-44050 610 0 610NA NA0 610NA
TCGA-05-4410-01TCGA-05-44100 0 0 0NA NA0 0NA
TCGA-05-4415-01TCGA-05-44151 91 1 91NA NA1 60NA
TCGA-05-4417-01TCGA-05-44170 455 0 455NA NA0 455NA
TCGA-05-4418-01TCGA-05-44181 274NA 274NA NA0 274NA
TCGA-05-4420-01TCGA-05-44200 912 0 912 0 9120 912NA
TCGA-05-4422-01TCGA-05-44220 365 0 365NA NA0 365NA
TCGA-05-4424-01TCGA-05-44240 913 0 913NA NA1 153NA
TCGA-05-4425-01TCGA-05-44250 669 0 669NA NA0 669NA
TCGA-05-4426-01TCGA-05-44260 791 0 791 1 4571 457NA
TCGA-05-4427-01TCGA-05-44270 791 0 791 0 7910 791NA
TCGA-05-4430-01TCGA-05-44300 761 0 761NA NA0 761NA
TCGA-05-4432-01TCGA-05-44320 761 0 761 0 7610 761NA
TCGA-05-4433-01TCGA-05-44330 730 0 730NA NA0 730NA
TCGA-05-4434-01TCGA-05-44341 457NA 457NA NA0 457NA
TCGA-05-5420-01TCGA-05-54200 457 0 457NA NA1 245NA
TCGA-05-5420-11TCGA-05-54200 457 0 457NA NA1 245NA
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
TCGA-LA-A7SW-01TCGA-LA-A7SW1 408 1 408NA NA1 235NA
TCGA-MF-A522-01TCGA-MF-A5221 360 1 360NA NA1 173NA
TCGA-NC-A5HD-01TCGA-NC-A5HD1 2 0 2NA NA0 2NA
TCGA-NC-A5HE-01TCGA-NC-A5HE02336 02336 0233602336NA
TCGA-NC-A5HF-01TCGA-NC-A5HF1 138 1 138NA NA1 132NA
TCGA-NC-A5HG-01TCGA-NC-A5HG01963 01963 0196301963NA
TCGA-NC-A5HH-01TCGA-NC-A5HH0 37 0 37 0 370 37NA
TCGA-NC-A5HI-01TCGA-NC-A5HI01743 01743 017431 70NA
TCGA-NC-A5HJ-01TCGA-NC-A5HJ1 418 1 418 0 4181 328NA
TCGA-NC-A5HK-01TCGA-NC-A5HK0 128 0 128 0 1280 128NA
TCGA-NC-A5HL-01TCGA-NC-A5HL1 88 0 88NA NA0 88NA
TCGA-NC-A5HM-01TCGA-NC-A5HM01212 01212 0121201212NA
TCGA-NC-A5HN-01TCGA-NC-A5HN01499 01499 0149901499NA
TCGA-NC-A5HO-01TCGA-NC-A5HO01336 01336 0133601336NA
TCGA-NC-A5HP-01TCGA-NC-A5HP1 770 1 770NA NA1 317NA
TCGA-NC-A5HQ-01TCGA-NC-A5HQ1 448NA 448 0 4480 448NA
TCGA-NC-A5HR-01TCGA-NC-A5HR01244 01244 0124401244NA
TCGA-NC-A5HT-01TCGA-NC-A5HT0 804 0 804 1 2031 203NA
TCGA-NK-A5CR-01TCGA-NK-A5CR02542 02542NA NA02542NA
TCGA-NK-A5CT-01TCGA-NK-A5CT01997 01997NA NA11990NA
TCGA-NK-A5CX-01TCGA-NK-A5CX0 111 0 111NA NA0 111NA
TCGA-NK-A5D1-01TCGA-NK-A5D10 511 0 511NA NA1 151NA
TCGA-NK-A7XE-01TCGA-NK-A7XE0 13 0 13NA NA0 13NA
TCGA-O2-A52N-01TCGA-O2-A52N11006 01006NA NA01006NA
TCGA-O2-A52Q-01TCGA-O2-A52Q1 113 1 113NA NA1 87NA
TCGA-O2-A52S-01TCGA-O2-A52S1 387 1 387NA NA1 246NA
TCGA-O2-A52V-01TCGA-O2-A52V11335 01335NA NA1 690NA
TCGA-O2-A52W-01TCGA-O2-A52W1 261 0 261NA NA0 261NA
TCGA-O2-A5IB-01TCGA-O2-A5IB1 340 1 340NA NA1 253NA
TCGA-XC-AA0X-01TCGA-XC-AA0X1 6 0 6NA NA0 6NA
In [1283]:
survival <- survival[match(names(score),survival$sample),]
survival$score <- score
In [1284]:
survival
A data.table: 1129 × 12
sample_PATIENTOSOS.timeDSSDSS.timeDFIDFI.timePFIPFI.timeRedactionscore
<chr><chr><int><int><int><int><int><int><int><int><lgl><dbl>
TCGA-NJ-A4YP-01TCGA-NJ-A4YP 0 50 0 50NA NA 0 50NA-0.0002982958
TCGA-18-3417-01TCGA-18-3417 11097 01097NA NA 01097NA 0.0258459039
TCGA-22-4613-01TCGA-22-4613 1 358NA 358NA NA 0 358NA 0.0148501111
TCGA-90-7769-01TCGA-90-7769 0 358 0 358 0 358 0 358NA 0.0244142264
TCGA-62-8397-01TCGA-62-8397 01289 01289 01289 01289NA-0.0053270352
TCGA-77-A5G1-01TCGA-77-A5G1 04026 04026 04026 12197NA 0.0278548672
TCGA-86-8278-01TCGA-86-8278 0 944 0 944 1 29 1 29NA 0.0095782341
TCGA-77-A5G3-01TCGA-77-A5G3 04570 04570 04570 04570NA 0.0218453711
TCGA-05-4430-01TCGA-05-4430 0 761 0 761NA NA 0 761NA 0.0150223382
TCGA-44-6778-01TCGA-44-6778 01864 01864 01864 01864NA 0.0131329978
TCGA-49-AARQ-01TCGA-49-AARQ 06732 06732 06732 06732NA-0.0059755051
TCGA-66-2766-01TCGA-66-2766 0 31 0 31NA NA 0 31NA 0.0232599838
TCGA-MP-A4TK-01TCGA-MP-A4TK 1 582 1 582 1 397 1 397NA 0.0106267321
TCGA-37-4135-01TCGA-37-4135 0 207 0 207NA NA 0 207NA 0.0105433144
TCGA-55-6975-01TCGA-55-6975 1 118 1 118NA NA 1 118NA 0.0215167339
TCGA-56-8201-01TCGA-56-8201 1 397 1 397NA NA 1 397NA 0.0141304397
NA NA NA NANA NANA NANA NANA 0.0093524792
TCGA-43-7656-01TCGA-43-7656 0 596 0 596 0 596 0 596NA 0.0140372338
TCGA-85-8481-01TCGA-85-8481 1 236 0 236NA NA 0 236NA 0.0331640891
TCGA-56-A4ZK-01TCGA-56-A4ZK 0 570 0 570 0 570 0 570NA 0.0208564429
NA NA NA NANA NANA NANA NANA 0.0112404426
TCGA-22-1011-01TCGA-22-1011 1 53 0 53NA NA 0 53NA 0.0247210733
TCGA-78-7160-01TCGA-78-7160 1 697NA 697NA NA 0 697NA 0.0063607608
TCGA-77-8156-01TCGA-77-8156 01106 01106 01106 01106NA 0.0118523309
TCGA-78-8655-01TCGA-78-8655 02360 02360 02360 02360NA 0.0052867446
TCGA-43-A475-01TCGA-43-A475 0 296 0 296 0 296 0 296NA 0.0080940485
TCGA-69-8255-01TCGA-69-8255 0 129 0 129NA NA 0 129NA-0.0035890406
TCGA-67-3773-01TCGA-67-3773 0 427 0 427NA NA 0 427NA 0.0030997943
TCGA-37-3789-01TCGA-37-3789 0 13 0 13NA NA 0 13NA 0.0256030702
TCGA-97-8552-01TCGA-97-8552 0 626 0 626 0 626 0 626NA 0.0048810961
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
TCGA-O2-A52S-01TCGA-O2-A52S 1 387 1 387NA NA 1 246NA 0.002323630
TCGA-33-4586-01TCGA-33-4586 1 428NA 428 1 169 1 169NA 0.012394929
TCGA-21-1077-01TCGA-21-1077 11058 11058NA NA 1 644NA 0.018856090
TCGA-60-2713-01TCGA-60-2713 01731 01731 11356 11356NA 0.018597410
TCGA-97-8172-01TCGA-97-8172 0 545 0 545 0 545 0 545NA 0.006024700
TCGA-L3-A524-01TCGA-L3-A524 1 490 0 490 0 490 0 490NA 0.011965620
TCGA-39-5021-01TCGA-39-5021 12086 12086 11875 11875NA 0.014908740
TCGA-85-A5B5-01TCGA-85-A5B5 0 111 0 111 0 111 0 111NA-0.001921737
TCGA-55-6981-11TCGA-55-6981 11379 01379 01379 01379NA 0.006153550
TCGA-58-A46L-01TCGA-58-A46L 01723 01723 01723 01723NA 0.013550788
TCGA-77-8144-01TCGA-77-8144 0 833 0 833 0 833 0 833NA 0.019250980
TCGA-J1-A4AH-01TCGA-J1-A4AH 0 581 0 581 0 581 0 581NA 0.007312451
TCGA-86-7713-01TCGA-86-7713 01157 01157 01157 01157NA 0.008360764
TCGA-78-7152-01TCGA-78-7152 11215 11215 11202 11202NA 0.005906560
TCGA-50-5946-01TCGA-50-5946 01617 01617NA NA 1 221NA 0.014507656
TCGA-56-7580-01TCGA-56-7580 0 925 0 925 0 925 0 925NA 0.019822712
TCGA-62-A46S-01TCGA-62-A46S 11653 11653 1 527 1 527NA-0.003521411
TCGA-95-7947-01TCGA-95-7947 0 477 0 477 0 477 0 477NA 0.022236653
TCGA-J2-8192-01TCGA-J2-8192 0 739 0 739NA NA 1 482NA 0.014575084
TCGA-55-6987-01TCGA-55-6987 02137 02137 02137 02137NA 0.004460157
TCGA-63-A5MY-01TCGA-63-A5MY 01052 01052 01052 01052NA 0.006623062
TCGA-50-6593-01TCGA-50-6593 1 336 1 336NA NA 1 266NA 0.009686103
TCGA-33-AASL-01TCGA-33-AASL 1 826 1 826NA NA 1 149NA 0.017489178
TCGA-85-A512-01TCGA-85-A512 0 465 0 465 0 465 0 465NA 0.010157524
TCGA-85-8354-01TCGA-85-8354 0 995 0 995 0 995 0 995NA 0.013494383
TCGA-O2-A5IB-01TCGA-O2-A5IB 1 340 1 340NA NA 1 253NA 0.012459250
TCGA-67-3771-01TCGA-67-3771 0 610 0 610NA NA 0 610NA 0.019927787
TCGA-77-7335-01TCGA-77-7335 12133NA2133NA NA 02133NA 0.015740704
TCGA-55-8302-01TCGA-55-8302 0 478 0 478 0 478 0 478NA 0.016035737
NA NA NA NANA NANA NANA NANA 0.013989310
In [1285]:
median(score)
mean(score)
0.012346634531988
0.0124252900406258
In [1286]:
survival$group<- ifelse((survival$score < 0.015), 1,2)
In [1287]:
table(survival$group)
  1   2 
710 419 
In [1288]:
fit.surv <-Surv(survival$OS.time,survival$OS)
km<-survfit(fit.surv~1,data = survival)
km_2<- survfit(fit.surv~group,data=survival)
In [1289]:
ggsurvplot (km)
No description has been provided for this image
In [1290]:
ggsurvplot(km_2,pval=TRUE)
No description has been provided for this image
In [1291]:
fit.surv <-Surv(survival$DFI.time,survival$DFI)
km_2<- survfit(fit.surv~group,data=survival)
In [1292]:
ggsurvplot(km_2,pval=TRUE)
No description has been provided for this image
In [1293]:
fit.surv <-Surv(survival$PFI.time,survival$PFI)
km_2<- survfit(fit.surv~group,data=survival)
In [1294]:
ggsurvplot(km_2,pval=TRUE)
No description has been provided for this image
In [1295]:
survival$group<- ifelse((survival$score < 0.021), 1,2)
In [1296]:
fit.surv <-Surv(survival$PFI.time,survival$PFI)
km_2<- survfit(fit.surv~group,data=survival)
ggsurvplot(km_2,pval=TRUE)
No description has been provided for this image
In [1297]:
egfr_sur <- survival %>% merge(.,GDC_phenotype[! GDC_phenotype$EGFR %in%  c('none',''),],by.x ='sample',by.y='sampleID',all = F)
In [1298]:
egfr_sur$group<- ifelse(egfr_sur$score < 0.02,1,2)
In [1299]:
fit.surv <-Surv(egfr_sur$OS.time,egfr_sur$OS)
km_2<- survfit(fit.surv~group,data=egfr_sur)
ggsurvplot(km_2,pval=TRUE)
No description has been provided for this image
In [1300]:
survival$group<- ifelse((survival$score < 0.021), 1,2)
In [1301]:
fit.surv <-Surv(survival$OS.time,survival$OS)
km_2<- survfit(fit.surv~group,data=survival)
In [1302]:
p <- ggsurvplot(km_2, main = "Survival curve",
           conf.int = TRUE,# 可信区间
           palette = "npg",# 支持ggsci配色,自定义颜色,brewer palettes中的配色,等
           ggtheme = theme_classic(), # 支持ggplot2及其扩展包的主题
           legend.title = "Score",#改变图例名称
           legend.labs = c("Low", "High"),
           risk.table = TRUE,        # 增加risk table
           #risk.table.title = "Risk set sizes",
           ncensor.plot = TRUE, #增加删失事件表
           surv.median.line = "hv",
           pval=TRUE,  #添加P值
           log.rank.weights = "1", #pval.method
)
p
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
No description has been provided for this image
In [1308]:
#https://github.com/kassambara/survminer/issues/152

# add method to grid.draw
grid.draw.ggsurvplot <- function(x){
  survminer:::print.ggsurvplot(x, newpage = FALSE)
}
# Remember to pass object `p`.
ggsave(
  filename ='survplot_TCGA_LUSC_byScore2.pdf',
  plot = p,
  device = 'pdf',
  #path = 'data/output',
  width = 8,
  height = 10
)
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
In [1309]:
getwd()
'/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master'
In [1312]:
ggsave(
  filename ='survplot_TCGA_LUSC_byScore2.pdf',
  plot = p,
  device = 'pdf',
  #path = 'data/output',
  width = 8,
  height = 11
)
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), :
“All aesthetics have length 1, but the data has 2 rows.
ℹ Did you mean to use `annotate()`?”